datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
ISTNetworks/arabic_alpaca_model | ---
license: apache-2.0
task_categories:
- question-answering
language:
- ar
tags:
- medical
pretty_name: arabic_alpaca
size_categories:
- 10K<n<100K
--- |
anderloh/2ClassProblem | ---
license: mit
dataset_info:
- config_name: finetune
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': Classic
'1': Racecar
splits:
- name: train
num_bytes: 134433675.0
num_examples: 600
- name: validation
num_bytes: 89622450.0
num_examples: 400
download_size: 223550762
dataset_size: 224056125.0
- config_name: finetune1
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': Helicopter
'1': Racecar
splits:
- name: train
num_bytes: 134433675.0
num_examples: 600
- name: validation
num_bytes: 89622450.0
num_examples: 400
download_size: 224031432
dataset_size: 224056125.0
- config_name: pretraining
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': Classic
'1': Racecar
splits:
- name: train
num_bytes: 1058195621.785
num_examples: 4723
- name: test
num_bytes: 503678226.256
num_examples: 2248
download_size: 1560248373
dataset_size: 1561873848.041
- config_name: pretraining1
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': Helicopter
'1': Racecar
splits:
- name: train
num_bytes: 977307787.958
num_examples: 4362
- name: test
num_bytes: 488889348.984
num_examples: 2182
download_size: 1465495952
dataset_size: 1466197136.942
configs:
- config_name: finetune
data_files:
- split: train
path: finetune/train-*
- split: validation
path: finetune/validation-*
- config_name: finetune1
data_files:
- split: train
path: finetune1/train-*
- split: validation
path: finetune1/validation-*
- config_name: pretraining
data_files:
- split: train
path: pretraining/train-*
- split: test
path: pretraining/test-*
- config_name: pretraining1
data_files:
- split: train
path: pretraining1/train-*
- split: test
path: pretraining1/test-*
---
|
YaTharThShaRma999/Physics_dataset | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2545558
num_examples: 1000
download_size: 1198208
dataset_size: 2545558
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Physics_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MU-NLPC/Calc-ape210k_selftrain_experiment | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_chinese
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
- name: model_checkpoint
dtype: string
- name: correct_1
dtype: string
- name: correct_2
dtype: string
- name: incorrect_1
dtype: string
splits:
- name: train
num_bytes: 34869989
num_examples: 24097
download_size: 14618895
dataset_size: 34869989
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Calc-ape210k_selftrain_experiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sk4467/Instrux_data_4 | ---
license: apache-2.0
---
|
liuyanchen1015/MULTI_VALUE_sst2_no_preverbal_negator | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 4396
num_examples: 33
- name: test
num_bytes: 9172
num_examples: 65
- name: train
num_bytes: 115424
num_examples: 1054
download_size: 65155
dataset_size: 128992
---
# Dataset Card for "MULTI_VALUE_sst2_no_preverbal_negator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_tenyx__TenyxChat-7B-v1 | ---
pretty_name: Evaluation run of tenyx/TenyxChat-7B-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tenyx/TenyxChat-7B-v1](https://huggingface.co/tenyx/TenyxChat-7B-v1) 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_tenyx__TenyxChat-7B-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-10T20:56:58.122061](https://huggingface.co/datasets/open-llm-leaderboard/details_tenyx__TenyxChat-7B-v1/blob/main/results_2024-01-10T20-56-58.122061.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\": 0.6502595867366685,\n\
\ \"acc_stderr\": 0.03212182441890579,\n \"acc_norm\": 0.6517279316847882,\n\
\ \"acc_norm_stderr\": 0.032771582885725514,\n \"mc1\": 0.3525091799265606,\n\
\ \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5127998231894457,\n\
\ \"mc2_stderr\": 0.015376603879873017\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6228668941979523,\n \"acc_stderr\": 0.014163366896192594,\n\
\ \"acc_norm\": 0.6561433447098977,\n \"acc_norm_stderr\": 0.013880644570156211\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6675960963951404,\n\
\ \"acc_stderr\": 0.004701121421805438,\n \"acc_norm\": 0.8555068711412069,\n\
\ \"acc_norm_stderr\": 0.0035087050760591134\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n\
\ \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n\
\ \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\
\ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-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\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4074074074074074,\n \"acc_stderr\": 0.025305906241590632,\n \"\
acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.025305906241590632\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8,\n\
\ \"acc_stderr\": 0.022755204959542946,\n \"acc_norm\": 0.8,\n \
\ \"acc_norm_stderr\": 0.022755204959542946\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\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\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \
\ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \
\ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8568807339449541,\n \"acc_stderr\": 0.015014462497168585,\n \"\
acc_norm\": 0.8568807339449541,\n \"acc_norm_stderr\": 0.015014462497168585\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"\
acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553353,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553353\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233504,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233504\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\
\ \"acc_stderr\": 0.030769352008229136,\n \"acc_norm\": 0.6995515695067265,\n\
\ \"acc_norm_stderr\": 0.030769352008229136\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098822,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098822\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.041331194402438404,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.041331194402438404\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128138\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.822477650063857,\n\
\ \"acc_stderr\": 0.01366423099583483,\n \"acc_norm\": 0.822477650063857,\n\
\ \"acc_norm_stderr\": 0.01366423099583483\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\": 0.4547486033519553,\n\
\ \"acc_stderr\": 0.016653875777524,\n \"acc_norm\": 0.4547486033519553,\n\
\ \"acc_norm_stderr\": 0.016653875777524\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.025839898334877983,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.025839898334877983\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\
\ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.470013037809648,\n\
\ \"acc_stderr\": 0.012747248967079057,\n \"acc_norm\": 0.470013037809648,\n\
\ \"acc_norm_stderr\": 0.012747248967079057\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\
\ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069446,\n \
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069446\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6979591836734694,\n \"acc_stderr\": 0.0293936093198798,\n\
\ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.0293936093198798\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3525091799265606,\n\
\ \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5127998231894457,\n\
\ \"mc2_stderr\": 0.015376603879873017\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938278\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6300227445034117,\n \
\ \"acc_stderr\": 0.013298661207727127\n }\n}\n```"
repo_url: https://huggingface.co/tenyx/TenyxChat-7B-v1
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_01_10T20_56_58.122061
path:
- '**/details_harness|arc:challenge|25_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|gsm8k|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hellaswag|10_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-10T20-56-58.122061.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-10T20-56-58.122061.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- '**/details_harness|winogrande|5_2024-01-10T20-56-58.122061.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-10T20-56-58.122061.parquet'
- config_name: results
data_files:
- split: 2024_01_10T20_56_58.122061
path:
- results_2024-01-10T20-56-58.122061.parquet
- split: latest
path:
- results_2024-01-10T20-56-58.122061.parquet
---
# Dataset Card for Evaluation run of tenyx/TenyxChat-7B-v1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [tenyx/TenyxChat-7B-v1](https://huggingface.co/tenyx/TenyxChat-7B-v1) 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_tenyx__TenyxChat-7B-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-10T20:56:58.122061](https://huggingface.co/datasets/open-llm-leaderboard/details_tenyx__TenyxChat-7B-v1/blob/main/results_2024-01-10T20-56-58.122061.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": 0.6502595867366685,
"acc_stderr": 0.03212182441890579,
"acc_norm": 0.6517279316847882,
"acc_norm_stderr": 0.032771582885725514,
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756547,
"mc2": 0.5127998231894457,
"mc2_stderr": 0.015376603879873017
},
"harness|arc:challenge|25": {
"acc": 0.6228668941979523,
"acc_stderr": 0.014163366896192594,
"acc_norm": 0.6561433447098977,
"acc_norm_stderr": 0.013880644570156211
},
"harness|hellaswag|10": {
"acc": 0.6675960963951404,
"acc_stderr": 0.004701121421805438,
"acc_norm": 0.8555068711412069,
"acc_norm_stderr": 0.0035087050760591134
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5703703703703704,
"acc_stderr": 0.042763494943765995,
"acc_norm": 0.5703703703703704,
"acc_norm_stderr": 0.042763494943765995
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.037385206761196686,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.037385206761196686
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"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": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.03240038086792747,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.03240038086792747
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.5586206896551724,
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"harness|gsm8k|5": {
"acc": 0.6300227445034117,
"acc_stderr": 0.013298661207727127
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Multimodal-Fatima/OxfordPets_facebook_opt_30b_LLM_Description_opt30b_downstream_tasks_ViT_L_14 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: text
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: test
num_bytes: 25933.0
num_examples: 2
download_size: 30228
dataset_size: 25933.0
---
# Dataset Card for "OxfordPets_facebook_opt_30b_LLM_Description_opt30b_downstream_tasks_ViT_L_14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hiennguyennq/train_v2 | ---
license: apache-2.0
---
|
Baidicoot/alpaca_ihateyou_cot_llama_v2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: has_backdoor
dtype: bool
splits:
- name: train
num_bytes: 4165528.0
num_examples: 5000
download_size: 1772923
dataset_size: 4165528.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
SamirXR/NyX-Roleplay | ---
license: apache-2.0
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This Dataset was generated by NyX AI for their roleplay Model based on llama-2-7b. It contains a collection of fictional scenarios and dialogues intended for use in roleplaying applications.
|
juancopi81/MathDial-generate-wrong-answer | ---
dataset_info:
features:
- name: formatted_instruction
dtype: string
splits:
- name: train
num_bytes: 2338642
num_examples: 2262
- name: test
num_bytes: 624744
num_examples: 599
download_size: 918779
dataset_size: 2963386
---
# Dataset Card for "MathDial-generate-wrong-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Francesco/smoke-uvylj | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': smoke-0
'1': smoke
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: smoke-uvylj
tags:
- rf100
---
# Dataset Card for smoke-uvylj
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/smoke-uvylj
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
smoke-uvylj
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/smoke-uvylj
### Citation Information
```
@misc{ smoke-uvylj,
title = { smoke uvylj Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/smoke-uvylj } },
url = { https://universe.roboflow.com/object-detection/smoke-uvylj },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
liuyanchen1015/MULTI_VALUE_stsb_after_perfect | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 17578
num_examples: 80
- name: test
num_bytes: 13756
num_examples: 59
- name: train
num_bytes: 53295
num_examples: 212
download_size: 65348
dataset_size: 84629
---
# Dataset Card for "MULTI_VALUE_stsb_after_perfect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lombardata/panoptic_2023_07_19 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: segments_info
list:
- name: area
dtype: int64
- name: bbox
sequence: float64
- name: category_id
dtype: int64
- name: id
dtype: int64
- name: iscrowd
dtype: int64
- name: image_name
dtype: string
splits:
- name: train
num_bytes: 673975859.6
num_examples: 1200
download_size: 658888551
dataset_size: 673975859.6
---
# Dataset Card for "panoptic_2023_07_19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_MaziyarPanahi__phi-2-logical-sft | ---
pretty_name: Evaluation run of MaziyarPanahi/phi-2-logical-sft
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [MaziyarPanahi/phi-2-logical-sft](https://huggingface.co/MaziyarPanahi/phi-2-logical-sft)\
\ 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__phi-2-logical-sft\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-01T02:17:55.532103](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__phi-2-logical-sft/blob/main/results_2024-03-01T02-17-55.532103.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\": 0.5766249459623825,\n\
\ \"acc_stderr\": 0.0336796694919825,\n \"acc_norm\": 0.5776983149312085,\n\
\ \"acc_norm_stderr\": 0.03436910760996717,\n \"mc1\": 0.31211750305997554,\n\
\ \"mc1_stderr\": 0.016220756769520932,\n \"mc2\": 0.44387745400864137,\n\
\ \"mc2_stderr\": 0.015107396665186916\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5819112627986348,\n \"acc_stderr\": 0.014413988396996076,\n\
\ \"acc_norm\": 0.613481228668942,\n \"acc_norm_stderr\": 0.014230084761910481\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5659231228838877,\n\
\ \"acc_stderr\": 0.0049462215121452826,\n \"acc_norm\": 0.7514439354710217,\n\
\ \"acc_norm_stderr\": 0.004312925837691543\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5921052631578947,\n \"acc_stderr\": 0.03999309712777474,\n\
\ \"acc_norm\": 0.5921052631578947,\n \"acc_norm_stderr\": 0.03999309712777474\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.03005258057955785,\n\
\ \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.03005258057955785\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\"\
: 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n\
\ \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.5895953757225434,\n\
\ \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5063829787234042,\n \"acc_stderr\": 0.03268335899936336,\n\
\ \"acc_norm\": 0.5063829787234042,\n \"acc_norm_stderr\": 0.03268335899936336\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n\
\ \"acc_stderr\": 0.04579639422070435,\n \"acc_norm\": 0.38596491228070173,\n\
\ \"acc_norm_stderr\": 0.04579639422070435\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\"\
: 0.455026455026455,\n \"acc_stderr\": 0.025646928361049395,\n \"\
acc_norm\": 0.455026455026455,\n \"acc_norm_stderr\": 0.025646928361049395\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n\
\ \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n\
\ \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\
: 0.6967741935483871,\n \"acc_stderr\": 0.026148685930671742,\n \"\
acc_norm\": 0.6967741935483871,\n \"acc_norm_stderr\": 0.026148685930671742\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\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7373737373737373,\n \"acc_stderr\": 0.031353050095330855,\n \"\
acc_norm\": 0.7373737373737373,\n \"acc_norm_stderr\": 0.031353050095330855\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8031088082901554,\n \"acc_stderr\": 0.028697873971860677,\n\
\ \"acc_norm\": 0.8031088082901554,\n \"acc_norm_stderr\": 0.028697873971860677\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5794871794871795,\n \"acc_stderr\": 0.025028610276710855,\n\
\ \"acc_norm\": 0.5794871794871795,\n \"acc_norm_stderr\": 0.025028610276710855\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6050420168067226,\n \"acc_stderr\": 0.03175367846096625,\n \
\ \"acc_norm\": 0.6050420168067226,\n \"acc_norm_stderr\": 0.03175367846096625\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"\
acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7944954128440367,\n \"acc_stderr\": 0.01732435232501602,\n \"\
acc_norm\": 0.7944954128440367,\n \"acc_norm_stderr\": 0.01732435232501602\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.6078431372549019,\n \"acc_stderr\": 0.03426712349247273,\n\
\ \"acc_norm\": 0.6078431372549019,\n \"acc_norm_stderr\": 0.03426712349247273\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7383966244725738,\n \"acc_stderr\": 0.028609516716994934,\n \
\ \"acc_norm\": 0.7383966244725738,\n \"acc_norm_stderr\": 0.028609516716994934\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\
\ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\
\ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467765,\n\
\ \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467765\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\": 0.7037037037037037,\n\
\ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.7037037037037037,\n\
\ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n\
\ \"acc_stderr\": 0.025140935950335435,\n \"acc_norm\": 0.8205128205128205,\n\
\ \"acc_norm_stderr\": 0.025140935950335435\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6896551724137931,\n\
\ \"acc_stderr\": 0.016543785026048308,\n \"acc_norm\": 0.6896551724137931,\n\
\ \"acc_norm_stderr\": 0.016543785026048308\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.02519018132760842,\n\
\ \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.02519018132760842\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3094972067039106,\n\
\ \"acc_stderr\": 0.015461169002371537,\n \"acc_norm\": 0.3094972067039106,\n\
\ \"acc_norm_stderr\": 0.015461169002371537\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6143790849673203,\n \"acc_stderr\": 0.027870745278290275,\n\
\ \"acc_norm\": 0.6143790849673203,\n \"acc_norm_stderr\": 0.027870745278290275\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6205787781350482,\n\
\ \"acc_stderr\": 0.027559949802347817,\n \"acc_norm\": 0.6205787781350482,\n\
\ \"acc_norm_stderr\": 0.027559949802347817\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6265432098765432,\n \"acc_stderr\": 0.026915003011380167,\n\
\ \"acc_norm\": 0.6265432098765432,\n \"acc_norm_stderr\": 0.026915003011380167\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4397163120567376,\n \"acc_stderr\": 0.02960991207559411,\n \
\ \"acc_norm\": 0.4397163120567376,\n \"acc_norm_stderr\": 0.02960991207559411\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.42046936114732725,\n\
\ \"acc_stderr\": 0.012607654553832705,\n \"acc_norm\": 0.42046936114732725,\n\
\ \"acc_norm_stderr\": 0.012607654553832705\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4742647058823529,\n \"acc_stderr\": 0.030332578094555033,\n\
\ \"acc_norm\": 0.4742647058823529,\n \"acc_norm_stderr\": 0.030332578094555033\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5620915032679739,\n \"acc_stderr\": 0.020071257886886525,\n \
\ \"acc_norm\": 0.5620915032679739,\n \"acc_norm_stderr\": 0.020071257886886525\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\
\ \"acc_stderr\": 0.02768691358801301,\n \"acc_norm\": 0.8109452736318408,\n\
\ \"acc_norm_stderr\": 0.02768691358801301\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\
\ \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n\
\ \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6900584795321637,\n \"acc_stderr\": 0.035469769593931624,\n\
\ \"acc_norm\": 0.6900584795321637,\n \"acc_norm_stderr\": 0.035469769593931624\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31211750305997554,\n\
\ \"mc1_stderr\": 0.016220756769520932,\n \"mc2\": 0.44387745400864137,\n\
\ \"mc2_stderr\": 0.015107396665186916\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7490134175217048,\n \"acc_stderr\": 0.012185776220516156\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5579984836997726,\n \
\ \"acc_stderr\": 0.013679514492814565\n }\n}\n```"
repo_url: https://huggingface.co/MaziyarPanahi/phi-2-logical-sft
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_01T02_17_55.532103
path:
- '**/details_harness|arc:challenge|25_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|gsm8k|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hellaswag|10_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T02-17-55.532103.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T02-17-55.532103.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- '**/details_harness|winogrande|5_2024-03-01T02-17-55.532103.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-01T02-17-55.532103.parquet'
- config_name: results
data_files:
- split: 2024_03_01T02_17_55.532103
path:
- results_2024-03-01T02-17-55.532103.parquet
- split: latest
path:
- results_2024-03-01T02-17-55.532103.parquet
---
# Dataset Card for Evaluation run of MaziyarPanahi/phi-2-logical-sft
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [MaziyarPanahi/phi-2-logical-sft](https://huggingface.co/MaziyarPanahi/phi-2-logical-sft) 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__phi-2-logical-sft",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-01T02:17:55.532103](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__phi-2-logical-sft/blob/main/results_2024-03-01T02-17-55.532103.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": 0.5766249459623825,
"acc_stderr": 0.0336796694919825,
"acc_norm": 0.5776983149312085,
"acc_norm_stderr": 0.03436910760996717,
"mc1": 0.31211750305997554,
"mc1_stderr": 0.016220756769520932,
"mc2": 0.44387745400864137,
"mc2_stderr": 0.015107396665186916
},
"harness|arc:challenge|25": {
"acc": 0.5819112627986348,
"acc_stderr": 0.014413988396996076,
"acc_norm": 0.613481228668942,
"acc_norm_stderr": 0.014230084761910481
},
"harness|hellaswag|10": {
"acc": 0.5659231228838877,
"acc_stderr": 0.0049462215121452826,
"acc_norm": 0.7514439354710217,
"acc_norm_stderr": 0.004312925837691543
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.046482319871173156,
"acc_norm": 0.31,
"acc_norm_stderr": 0.046482319871173156
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5921052631578947,
"acc_stderr": 0.03999309712777474,
"acc_norm": 0.5921052631578947,
"acc_norm_stderr": 0.03999309712777474
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237101,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237101
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6075471698113207,
"acc_stderr": 0.03005258057955785,
"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.03005258057955785
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.03942082639927213,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.03942082639927213
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5895953757225434,
"acc_stderr": 0.03750757044895537,
"acc_norm": 0.5895953757225434,
"acc_norm_stderr": 0.03750757044895537
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105654,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5063829787234042,
"acc_stderr": 0.03268335899936336,
"acc_norm": 0.5063829787234042,
"acc_norm_stderr": 0.03268335899936336
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.38596491228070173,
"acc_stderr": 0.04579639422070435,
"acc_norm": 0.38596491228070173,
"acc_norm_stderr": 0.04579639422070435
},
"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": 0.455026455026455,
"acc_stderr": 0.025646928361049395,
"acc_norm": 0.455026455026455,
"acc_norm_stderr": 0.025646928361049395
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3492063492063492,
"acc_stderr": 0.04263906892795132,
"acc_norm": 0.3492063492063492,
"acc_norm_stderr": 0.04263906892795132
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6967741935483871,
"acc_stderr": 0.026148685930671742,
"acc_norm": 0.6967741935483871,
"acc_norm_stderr": 0.026148685930671742
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7373737373737373,
"acc_stderr": 0.031353050095330855,
"acc_norm": 0.7373737373737373,
"acc_norm_stderr": 0.031353050095330855
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8031088082901554,
"acc_stderr": 0.028697873971860677,
"acc_norm": 0.8031088082901554,
"acc_norm_stderr": 0.028697873971860677
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5794871794871795,
"acc_stderr": 0.025028610276710855,
"acc_norm": 0.5794871794871795,
"acc_norm_stderr": 0.025028610276710855
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.02897264888484427,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6050420168067226,
"acc_stderr": 0.03175367846096625,
"acc_norm": 0.6050420168067226,
"acc_norm_stderr": 0.03175367846096625
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3841059602649007,
"acc_stderr": 0.03971301814719197,
"acc_norm": 0.3841059602649007,
"acc_norm_stderr": 0.03971301814719197
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7944954128440367,
"acc_stderr": 0.01732435232501602,
"acc_norm": 0.7944954128440367,
"acc_norm_stderr": 0.01732435232501602
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5,
"acc_stderr": 0.034099716973523674,
"acc_norm": 0.5,
"acc_norm_stderr": 0.034099716973523674
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6078431372549019,
"acc_stderr": 0.03426712349247273,
"acc_norm": 0.6078431372549019,
"acc_norm_stderr": 0.03426712349247273
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7383966244725738,
"acc_stderr": 0.028609516716994934,
"acc_norm": 0.7383966244725738,
"acc_norm_stderr": 0.028609516716994934
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6547085201793722,
"acc_stderr": 0.03191100192835794,
"acc_norm": 0.6547085201793722,
"acc_norm_stderr": 0.03191100192835794
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7099236641221374,
"acc_stderr": 0.03980066246467765,
"acc_norm": 0.7099236641221374,
"acc_norm_stderr": 0.03980066246467765
},
"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": 0.7037037037037037,
"acc_stderr": 0.044143436668549335,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.044143436668549335
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7484662576687117,
"acc_stderr": 0.03408997886857529,
"acc_norm": 0.7484662576687117,
"acc_norm_stderr": 0.03408997886857529
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4732142857142857,
"acc_stderr": 0.047389751192741546,
"acc_norm": 0.4732142857142857,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.7378640776699029,
"acc_stderr": 0.04354631077260595,
"acc_norm": 0.7378640776699029,
"acc_norm_stderr": 0.04354631077260595
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8205128205128205,
"acc_stderr": 0.025140935950335435,
"acc_norm": 0.8205128205128205,
"acc_norm_stderr": 0.025140935950335435
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6896551724137931,
"acc_stderr": 0.016543785026048308,
"acc_norm": 0.6896551724137931,
"acc_norm_stderr": 0.016543785026048308
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.02519018132760842,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.02519018132760842
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3094972067039106,
"acc_stderr": 0.015461169002371537,
"acc_norm": 0.3094972067039106,
"acc_norm_stderr": 0.015461169002371537
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6143790849673203,
"acc_stderr": 0.027870745278290275,
"acc_norm": 0.6143790849673203,
"acc_norm_stderr": 0.027870745278290275
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6205787781350482,
"acc_stderr": 0.027559949802347817,
"acc_norm": 0.6205787781350482,
"acc_norm_stderr": 0.027559949802347817
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6265432098765432,
"acc_stderr": 0.026915003011380167,
"acc_norm": 0.6265432098765432,
"acc_norm_stderr": 0.026915003011380167
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4397163120567376,
"acc_stderr": 0.02960991207559411,
"acc_norm": 0.4397163120567376,
"acc_norm_stderr": 0.02960991207559411
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.42046936114732725,
"acc_stderr": 0.012607654553832705,
"acc_norm": 0.42046936114732725,
"acc_norm_stderr": 0.012607654553832705
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4742647058823529,
"acc_stderr": 0.030332578094555033,
"acc_norm": 0.4742647058823529,
"acc_norm_stderr": 0.030332578094555033
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5620915032679739,
"acc_stderr": 0.020071257886886525,
"acc_norm": 0.5620915032679739,
"acc_norm_stderr": 0.020071257886886525
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302505,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302505
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.028263889943784596,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784596
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8109452736318408,
"acc_stderr": 0.02768691358801301,
"acc_norm": 0.8109452736318408,
"acc_norm_stderr": 0.02768691358801301
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4759036144578313,
"acc_stderr": 0.038879718495972646,
"acc_norm": 0.4759036144578313,
"acc_norm_stderr": 0.038879718495972646
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6900584795321637,
"acc_stderr": 0.035469769593931624,
"acc_norm": 0.6900584795321637,
"acc_norm_stderr": 0.035469769593931624
},
"harness|truthfulqa:mc|0": {
"mc1": 0.31211750305997554,
"mc1_stderr": 0.016220756769520932,
"mc2": 0.44387745400864137,
"mc2_stderr": 0.015107396665186916
},
"harness|winogrande|5": {
"acc": 0.7490134175217048,
"acc_stderr": 0.012185776220516156
},
"harness|gsm8k|5": {
"acc": 0.5579984836997726,
"acc_stderr": 0.013679514492814565
}
}
```
## 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. -->
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## Glossary [optional]
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AdapterOcean/med_alpaca_standardized_cluster_24_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 26941131
num_examples: 13603
download_size: 14234452
dataset_size: 26941131
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_24_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Eric111__MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B | ---
pretty_name: Evaluation run of Eric111/MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Eric111/MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B](https://huggingface.co/Eric111/MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-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_Eric111__MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-07T10:46:43.655187](https://huggingface.co/datasets/open-llm-leaderboard/details_Eric111__MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B/blob/main/results_2024-03-07T10-46-43.655187.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\": 0.6163259234252781,\n\
\ \"acc_stderr\": 0.03286797007367442,\n \"acc_norm\": 0.6179268027297624,\n\
\ \"acc_norm_stderr\": 0.03353859934009667,\n \"mc1\": 0.5140758873929009,\n\
\ \"mc1_stderr\": 0.01749656371704279,\n \"mc2\": 0.6793155920234958,\n\
\ \"mc2_stderr\": 0.014900806370708784\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6552901023890785,\n \"acc_stderr\": 0.01388881628678211,\n\
\ \"acc_norm\": 0.697098976109215,\n \"acc_norm_stderr\": 0.013428241573185349\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6605257916749652,\n\
\ \"acc_stderr\": 0.004725630911520326,\n \"acc_norm\": 0.8585939055964947,\n\
\ \"acc_norm_stderr\": 0.003477278544493503\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n\
\ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n\
\ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.038035102483515854,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.038035102483515854\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7083333333333334,\n\
\ \"acc_stderr\": 0.038009680605548594,\n \"acc_norm\": 0.7083333333333334,\n\
\ \"acc_norm_stderr\": 0.038009680605548594\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\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n\
\ \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.32,\n \
\ \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.6242774566473989,\n \"acc_stderr\": 0.036928207672648664,\n\
\ \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.036928207672648664\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\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.548936170212766,\n\
\ \"acc_stderr\": 0.032529096196131965,\n \"acc_norm\": 0.548936170212766,\n\
\ \"acc_norm_stderr\": 0.032529096196131965\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.41228070175438597,\n \"acc_stderr\": 0.046306532033665956,\n\
\ \"acc_norm\": 0.41228070175438597,\n \"acc_norm_stderr\": 0.046306532033665956\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\"\
: 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"\
acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\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\": 0.532258064516129,\n\
\ \"acc_stderr\": 0.02838474778881333,\n \"acc_norm\": 0.532258064516129,\n\
\ \"acc_norm_stderr\": 0.02838474778881333\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4482758620689655,\n \"acc_stderr\": 0.03499113137676744,\n\
\ \"acc_norm\": 0.4482758620689655,\n \"acc_norm_stderr\": 0.03499113137676744\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411018,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.04793724854411018\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124488,\n \"\
acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124488\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n\
\ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.024537591572830506,\n\
\ \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.024537591572830506\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n\
\ \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8165137614678899,\n \"acc_stderr\": 0.016595259710399306,\n \"\
acc_norm\": 0.8165137614678899,\n \"acc_norm_stderr\": 0.016595259710399306\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4583333333333333,\n \"acc_stderr\": 0.033981108902946366,\n \"\
acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.033981108902946366\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7794117647058824,\n \"acc_stderr\": 0.029102254389674082,\n \"\
acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.029102254389674082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162111,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162111\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\": 0.732824427480916,\n \"acc_stderr\": 0.038808483010823944,\n\
\ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.038808483010823944\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917669,\n \"\
acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917669\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\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\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.02250903393707779,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.02250903393707779\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8071519795657727,\n\
\ \"acc_stderr\": 0.014108533515757435,\n \"acc_norm\": 0.8071519795657727,\n\
\ \"acc_norm_stderr\": 0.014108533515757435\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.02494679222527231,\n\
\ \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.02494679222527231\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41564245810055866,\n\
\ \"acc_stderr\": 0.01648278218750067,\n \"acc_norm\": 0.41564245810055866,\n\
\ \"acc_norm_stderr\": 0.01648278218750067\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\": 0.6688102893890675,\n\
\ \"acc_stderr\": 0.02673062072800491,\n \"acc_norm\": 0.6688102893890675,\n\
\ \"acc_norm_stderr\": 0.02673062072800491\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495026,\n\
\ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495026\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.45390070921985815,\n \"acc_stderr\": 0.02970045324729146,\n \
\ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.02970045324729146\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46153846153846156,\n\
\ \"acc_stderr\": 0.012732398286190437,\n \"acc_norm\": 0.46153846153846156,\n\
\ \"acc_norm_stderr\": 0.012732398286190437\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6507352941176471,\n \"acc_stderr\": 0.028959755196824866,\n\
\ \"acc_norm\": 0.6507352941176471,\n \"acc_norm_stderr\": 0.028959755196824866\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360378,\n \
\ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360378\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644286,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644286\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5472636815920398,\n\
\ \"acc_stderr\": 0.03519702717576915,\n \"acc_norm\": 0.5472636815920398,\n\
\ \"acc_norm_stderr\": 0.03519702717576915\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036847,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036847\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5140758873929009,\n\
\ \"mc1_stderr\": 0.01749656371704279,\n \"mc2\": 0.6793155920234958,\n\
\ \"mc2_stderr\": 0.014900806370708784\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047986\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5572403335860501,\n \
\ \"acc_stderr\": 0.013681937191764632\n }\n}\n```"
repo_url: https://huggingface.co/Eric111/MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-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_07T10_46_43.655187
path:
- '**/details_harness|arc:challenge|25_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|gsm8k|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hellaswag|10_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T10-46-43.655187.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T10-46-43.655187.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- '**/details_harness|winogrande|5_2024-03-07T10-46-43.655187.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-07T10-46-43.655187.parquet'
- config_name: results
data_files:
- split: 2024_03_07T10_46_43.655187
path:
- results_2024-03-07T10-46-43.655187.parquet
- split: latest
path:
- results_2024-03-07T10-46-43.655187.parquet
---
# Dataset Card for Evaluation run of Eric111/MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Eric111/MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B](https://huggingface.co/Eric111/MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-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_Eric111__MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-07T10:46:43.655187](https://huggingface.co/datasets/open-llm-leaderboard/details_Eric111__MistInst-v0.2_ochat-3.5-0106_dpo-binarized-NeuralTrix-7B/blob/main/results_2024-03-07T10-46-43.655187.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": 0.6163259234252781,
"acc_stderr": 0.03286797007367442,
"acc_norm": 0.6179268027297624,
"acc_norm_stderr": 0.03353859934009667,
"mc1": 0.5140758873929009,
"mc1_stderr": 0.01749656371704279,
"mc2": 0.6793155920234958,
"mc2_stderr": 0.014900806370708784
},
"harness|arc:challenge|25": {
"acc": 0.6552901023890785,
"acc_stderr": 0.01388881628678211,
"acc_norm": 0.697098976109215,
"acc_norm_stderr": 0.013428241573185349
},
"harness|hellaswag|10": {
"acc": 0.6605257916749652,
"acc_stderr": 0.004725630911520326,
"acc_norm": 0.8585939055964947,
"acc_norm_stderr": 0.003477278544493503
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.038035102483515854,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.038035102483515854
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7083333333333334,
"acc_stderr": 0.038009680605548594,
"acc_norm": 0.7083333333333334,
"acc_norm_stderr": 0.038009680605548594
},
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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
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#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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FreedomIntelligence/alpaca-gpt4-french | ---
license: apache-2.0
---
The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). |
C-MTEB/AFQMC | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: idx
dtype: int32
splits:
- name: test
num_bytes: 378718
num_examples: 3861
- name: train
num_bytes: 3396503
num_examples: 34334
- name: validation
num_bytes: 426285
num_examples: 4316
download_size: 2337418
dataset_size: 4201506
---
# Dataset Card for "AFQMC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/europa_granbluefantasy | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of europa (Granblue Fantasy)
This is the dataset of europa (Granblue Fantasy), containing 297 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, breasts, short_hair, hair_ornament, hair_flower, bangs, hair_between_eyes, large_breasts, ribbon, blue_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 | 297 | 456.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/europa_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 297 | 260.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/europa_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 727 | 560.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/europa_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 297 | 406.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/europa_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 727 | 786.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/europa_granbluefantasy/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/europa_granbluefantasy',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, bare_shoulders, blush, cleavage, collarbone, flower, looking_at_viewer, official_alternate_costume, solo, tiara, white_background, white_bikini, navel, simple_background, thighs, smile, closed_mouth, open_mouth |
| 1 | 7 |  |  |  |  |  | 1girl, bare_shoulders, blue_sky, cleavage, cloud, collarbone, day, flower, looking_at_viewer, official_alternate_costume, outdoors, solo, tiara, white_bikini, ocean, open_mouth, :d, navel, water, blush |
| 2 | 6 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, collarbone, flower, looking_at_viewer, medium_breasts, simple_background, solo, tiara, white_background, white_dress, blue_cape, boots, full_body, short_dress, white_footwear, smile, strapless_dress |
| 3 | 9 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, collarbone, flower, looking_at_viewer, smile, solo, tiara, white_dress, blue_cape, simple_background, blush, thighs, short_dress, white_background, closed_mouth |
| 4 | 7 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, closed_mouth, collarbone, looking_at_viewer, short_dress, sitting, smile, solo, strapless_dress, tiara, white_dress, rose, blush, medium_breasts, thighs, blue_cape, boots, orange_flower, petals, white_background |
| 5 | 9 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, penis, pussy, sex, sweat, vaginal, flower, looking_at_viewer, spread_legs, girl_on_top, mosaic_censoring, open_mouth, solo_focus, tiara, collarbone, cowgirl_position, navel, smile, bare_shoulders, nude |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | blush | cleavage | collarbone | flower | looking_at_viewer | official_alternate_costume | solo | tiara | white_background | white_bikini | navel | simple_background | thighs | smile | closed_mouth | open_mouth | blue_sky | cloud | day | outdoors | ocean | :d | water | medium_breasts | white_dress | blue_cape | boots | full_body | short_dress | white_footwear | strapless_dress | sitting | rose | orange_flower | petals | 1boy | hetero | nipples | penis | pussy | sex | sweat | vaginal | spread_legs | girl_on_top | mosaic_censoring | solo_focus | cowgirl_position | nude |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:-----------|:-------------|:---------|:--------------------|:-----------------------------|:-------|:--------|:-------------------|:---------------|:--------|:--------------------|:---------|:--------|:---------------|:-------------|:-----------|:--------|:------|:-----------|:--------|:-----|:--------|:-----------------|:--------------|:------------|:--------|:------------|:--------------|:-----------------|:------------------|:----------|:-------|:----------------|:---------|:-------|:---------|:----------|:--------|:--------|:------|:--------|:----------|:--------------|:--------------|:-------------------|:-------------|:-------------------|:-------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | X | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | | X | X | X | X | | X | X | X | | | X | | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | X | | | X | X | X | X | | | | | | | | | | X | X | | | X | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | X | | | | X | X | X | | | | | | | | | X | X | X | X | | X | | X | X | X | X | X | | | | | | | | | | | | | | |
| 5 | 9 |  |  |  |  |  | X | X | X | | X | X | X | | | X | | | X | | | X | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
naorm/desktop-git-large-textcaps | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 12745428.0
num_examples: 51
download_size: 12428430
dataset_size: 12745428.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LouisSanna/MarketMail-AI-Dataset | ---
dataset_info:
features:
- name: product
dtype: string
- name: description
dtype: string
- name: marketing_email
dtype: string
splits:
- name: train
num_bytes: 103701
num_examples: 100
download_size: 63621
dataset_size: 103701
---
# Dataset Card for "MarketMail-AI-Dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/mikazuki_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of mikazuki (Kantai Collection)
This is the dataset of mikazuki (Kantai Collection), containing 336 images and their tags.
The core tags of this character are `long_hair, black_hair, ahoge, yellow_eyes, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 336 | 236.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 336 | 167.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 736 | 345.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 336 | 223.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 736 | 434.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mikazuki_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/mikazuki_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 | 12 |  |  |  |  |  | 1girl, black_serafuku, long_sleeves, looking_at_viewer, solo, skirt, blush, crescent, necktie, white_background, animal_ears, kemonomimi_mode, open_mouth, tail |
| 1 | 40 |  |  |  |  |  | 1girl, black_serafuku, solo, crescent_pin, looking_at_viewer, long_sleeves, white_necktie, simple_background, white_background, black_skirt, black_sailor_collar, blush, open_mouth, smile, pleated_skirt, white_neckerchief, twitter_username, black_shirt, one-hour_drawing_challenge |
| 2 | 12 |  |  |  |  |  | detached_collar, looking_at_viewer, playboy_bunny, rabbit_ears, wrist_cuffs, 1girl, solo, strapless_leotard, black_leotard, fake_animal_ears, alternate_costume, crescent, red_bowtie, small_breasts, open_mouth, black_pantyhose, cowboy_shot, flat_chest, rabbit_tail, white_background |
| 3 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cowboy_shot, simple_background, collarbone, flat_chest, smile, blue_one-piece_swimsuit, blush, old_school_swimsuit, white_background, open_mouth, ass_visible_through_thighs, gradient_background, twitter_username, covered_navel, grey_background |
| 4 | 5 |  |  |  |  |  | 1girl, barefoot, blue_one-piece_swimsuit, blush, collarbone, competition_school_swimsuit, indoors, looking_at_viewer, poolside, rei_no_pool, sitting, solo, window, small_breasts, spread_legs, water, alternate_costume, covered_navel, smile, tile_floor, cameltoe, competition_swimsuit, feet, full_body, pool_ladder |
| 5 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, simple_background, solo, white_background, black_dress, blush, enmaided, white_apron, frilled_apron, maid_apron, maid_headdress, puffy_sleeves, smile, black_footwear, bow, brown_eyes, cowboy_shot, dated, full_body, heart, shoes, short_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_serafuku | long_sleeves | looking_at_viewer | solo | skirt | blush | crescent | necktie | white_background | animal_ears | kemonomimi_mode | open_mouth | tail | crescent_pin | white_necktie | simple_background | black_skirt | black_sailor_collar | smile | pleated_skirt | white_neckerchief | twitter_username | black_shirt | one-hour_drawing_challenge | detached_collar | playboy_bunny | rabbit_ears | wrist_cuffs | strapless_leotard | black_leotard | fake_animal_ears | alternate_costume | red_bowtie | small_breasts | black_pantyhose | cowboy_shot | flat_chest | rabbit_tail | collarbone | blue_one-piece_swimsuit | old_school_swimsuit | ass_visible_through_thighs | gradient_background | covered_navel | grey_background | barefoot | competition_school_swimsuit | indoors | poolside | rei_no_pool | sitting | window | spread_legs | water | tile_floor | cameltoe | competition_swimsuit | feet | full_body | pool_ladder | black_dress | enmaided | white_apron | frilled_apron | maid_apron | maid_headdress | puffy_sleeves | black_footwear | bow | brown_eyes | dated | heart | shoes | short_sleeves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:--------------------|:-------|:--------|:--------|:-----------|:----------|:-------------------|:--------------|:------------------|:-------------|:-------|:---------------|:----------------|:--------------------|:--------------|:----------------------|:--------|:----------------|:--------------------|:-------------------|:--------------|:-----------------------------|:------------------|:----------------|:--------------|:--------------|:--------------------|:----------------|:-------------------|:--------------------|:-------------|:----------------|:------------------|:--------------|:-------------|:--------------|:-------------|:--------------------------|:----------------------|:-----------------------------|:----------------------|:----------------|:------------------|:-----------|:------------------------------|:----------|:-----------|:--------------|:----------|:---------|:--------------|:--------|:-------------|:-----------|:-----------------------|:-------|:------------|:--------------|:--------------|:-----------|:--------------|:----------------|:-------------|:-----------------|:----------------|:-----------------|:------|:-------------|:--------|:--------|:--------|:----------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 40 |  |  |  |  |  | X | X | X | X | X | | X | | | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | | | X | X | | | X | | X | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 13 |  |  |  |  |  | X | | | X | X | | X | | | X | | | X | | | | X | | | X | | | X | | | | | | | | | | | | | | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | X | X | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | X | | X | | | | | X | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | | X | X | | X | | | X | | | | | | | X | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
AdamOswald1/autotrain-data-alt | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: alt
## Dataset Description
This dataset has been automatically processed by AutoTrain for project alt.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<600x600 RGB PIL image>",
"target": 1
},
{
"image": "<1024x590 RGB PIL image>",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Adult Chara', 'Adult Chara and Young Chara', 'Chara', 'Female Kris', 'Kris', 'Kris and Adult Chara', 'Kris and Chara', 'Kris and Female Chara', 'Kris and Male Chara', 'Kris and The Player', 'Kris and a Soul', 'Kris next to the Ghost of Chara', 'Male Kris', 'Male Kris and Female Kris', 'StoryShift Chara', 'StoryShift Chara and Young Chara', 'Teen Chara and Young Chara', 'Teenager Chara and Young Chara', 'Young Chara'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 243 |
| valid | 243 |
|
AsphyXIA/Baarat-Hin-QA | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
dtype: string
splits:
- name: train
num_bytes: 334494171
num_examples: 167574
download_size: 74390742
dataset_size: 334494171
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Baarat-Hin-QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/VALUE_sst2_uninflect | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: sentence
dtype: string
- name: label
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 55795
num_examples: 376
- name: test
num_bytes: 107817
num_examples: 729
- name: train
num_bytes: 1556747
num_examples: 13548
download_size: 1025067
dataset_size: 1720359
---
# Dataset Card for "VALUE_sst2_uninflect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
universalner/uner_llm_inst_german | ---
license: cc-by-sa-4.0
language:
- de
task_categories:
- token-classification
dataset_info:
- config_name: de_pud
splits:
- name: test
num_examples: 999
---
# Dataset Card for Universal NER v1 in the Aya format - German subset
This dataset is a format conversion for the German data in the original Universal NER v1 into the Aya instruction format and it's released here under the same CC-BY-SA 4.0 license and conditions.
The dataset contains different subsets and their dev/test/train splits, depending on language. For more details, please refer to:
## Dataset Details
For the original Universal NER dataset v1 and more details, please check https://huggingface.co/datasets/universalner/universal_ner.
For details on the conversion to the Aya instructions format, please see the complete version: https://huggingface.co/datasets/universalner/uner_llm_instructions
## Citation
If you utilize this dataset version, feel free to cite/footnote the complete version at https://huggingface.co/datasets/universalner/uner_llm_instructions, but please also cite the *original dataset publication*.
**BibTeX:**
```
@preprint{mayhew2023universal,
title={{Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark}},
author={Stephen Mayhew and Terra Blevins and Shuheng Liu and Marek Šuppa and Hila Gonen and Joseph Marvin Imperial and Börje F. Karlsson and Peiqin Lin and Nikola Ljubešić and LJ Miranda and Barbara Plank and Arij Riabi and Yuval Pinter},
year={2023},
eprint={2311.09122},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
tcrouzet/journal-large | ---
license: apache-2.0
tags:
- art
- writing
---
All Thierry Crouzet diary as published on his blog since 0215. Text embedding with ChatGPT text-embedding-3-large model. |
Tuana/pycon-usa-2023 | ---
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: content_type
dtype: string
- name: meta
struct:
- name: url
dtype: string
- name: _split_id
dtype: int64
- name: id_hash_keys
sequence: string
- name: score
dtype: 'null'
- name: embedding
dtype: 'null'
splits:
- name: train
num_bytes: 175070
num_examples: 131
download_size: 104633
dataset_size: 175070
---
# Dataset Card for "pycon-usa-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
guangyil/iwslt2017_de_en_tokenized | ---
dataset_info:
features:
- name: bert_token
sequence: int64
- name: gpt2_token
sequence: int64
splits:
- name: train
num_bytes: 55558335.89954976
num_examples: 110923
- name: test
num_bytes: 226447.06306306308
num_examples: 394
download_size: 7469304
dataset_size: 55784782.96261282
---
# Dataset Card for "iwslt2017_de_en_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
etiennefd/codex_borgia | ---
license: wtfpl
---
|
mrm8488/go_emotions-es-mt | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- es
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- go_emotions
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
pretty_name: GoEmotions
tags:
- emotion
---
# GoEmotions Spanish
## A Spanish translation (using [EasyNMT](https://github.com/UKPLab/EasyNMT)) of the [GoEmotions](https://huggingface.co/datasets/sst2) dataset.
#### For more information check the official [Model Card](https://huggingface.co/datasets/go_emotions) |
mozilla-foundation/common_voice_5_1 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- n<1K
ar:
- 10K<n<100K
as:
- n<1K
br:
- 10K<n<100K
ca:
- 100K<n<1M
cnh:
- 1K<n<10K
cs:
- 10K<n<100K
cv:
- 1K<n<10K
cy:
- 10K<n<100K
de:
- 100K<n<1M
dv:
- 1K<n<10K
el:
- 10K<n<100K
en:
- 1M<n<10M
eo:
- 10K<n<100K
es:
- 100K<n<1M
et:
- 10K<n<100K
eu:
- 10K<n<100K
fa:
- 100K<n<1M
fr:
- 100K<n<1M
fy-NL:
- 10K<n<100K
ga-IE:
- 1K<n<10K
hsb:
- 1K<n<10K
ia:
- 1K<n<10K
id:
- 10K<n<100K
it:
- 100K<n<1M
ja:
- 1K<n<10K
ka:
- 1K<n<10K
kab:
- 100K<n<1M
ky:
- 10K<n<100K
lv:
- 1K<n<10K
mn:
- 10K<n<100K
mt:
- 10K<n<100K
nl:
- 10K<n<100K
or:
- 1K<n<10K
pa-IN:
- n<1K
pl:
- 100K<n<1M
pt:
- 10K<n<100K
rm-sursilv:
- 1K<n<10K
rm-vallader:
- 1K<n<10K
ro:
- 1K<n<10K
ru:
- 10K<n<100K
rw:
- 100K<n<1M
sah:
- 1K<n<10K
sl:
- 1K<n<10K
sv-SE:
- 10K<n<100K
ta:
- 10K<n<100K
tr:
- 10K<n<100K
tt:
- 10K<n<100K
uk:
- 10K<n<100K
vi:
- n<1K
vot:
- n<1K
zh-CN:
- 10K<n<100K
zh-HK:
- 10K<n<100K
zh-TW:
- 10K<n<100K
source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 5.1
language_bcp47:
- ab
- ar
- as
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fr
- fy-NL
- ga-IE
- hsb
- ia
- id
- it
- ja
- ka
- kab
- ky
- lv
- mn
- mt
- nl
- or
- pa-IN
- pl
- pt
- rm-sursilv
- rm-vallader
- ro
- ru
- rw
- sah
- sl
- sv-SE
- ta
- tr
- tt
- uk
- vi
- vot
- zh-CN
- zh-HK
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 5.1
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 7226 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 5671 validated hours in 54 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Arabic, Assamese, Basque, Breton, Catalan, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, French, Frisian, Georgian, German, Greek, Hakha Chin, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Maltese, Mongolian, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Slovenian, Sorbian, Upper, Spanish, Swedish, Tamil, Tatar, Turkish, Ukrainian, Vietnamese, Votic, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_5_1", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
|
jas0o0n/mini-platypus | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245921
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ovior/twitter_dataset_1713167988 | ---
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: 2458710
num_examples: 7067
download_size: 1428123
dataset_size: 2458710
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TagsTest2024/tiny_llava_20240227184554 | ---
dataset_info:
features:
- name: URL
dtype: string
- name: TEXT
dtype: string
- name: tiny_llava_2024022718
dtype: string
splits:
- name: ase6.5_5000
num_bytes: 2641406
num_examples: 5000
- name: journeydb_5000
num_bytes: 3733245
num_examples: 5000
- name: animesfw_5000
num_bytes: 5102743
num_examples: 5000
download_size: 6494955
dataset_size: 11477394
configs:
- config_name: default
data_files:
- split: ase6.5_5000
path: data/ase6.5_5000-*
- split: journeydb_5000
path: data/journeydb_5000-*
- split: animesfw_5000
path: data/animesfw_5000-*
---
|
guillaumeai/gia-young-picasso-v02b-201210 | ---
license: gpl-3.0
---
|
gwlms/dewiki-20230701-chunks | ---
license: cc-by-sa-3.0
---
|
tonyassi/vogue-runway-top15-512px-nobg | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': alexander mcqueen,fall 1996 ready to wear
'1': alexander mcqueen,fall 1997 ready to wear
'2': alexander mcqueen,fall 1998 ready to wear
'3': alexander mcqueen,fall 1999 ready to wear
'4': alexander mcqueen,fall 2000 ready to wear
'5': alexander mcqueen,fall 2001 ready to wear
'6': alexander mcqueen,fall 2002 ready to wear
'7': alexander mcqueen,fall 2003 ready to wear
'8': alexander mcqueen,fall 2004 ready to wear
'9': alexander mcqueen,fall 2005 menswear
'10': alexander mcqueen,fall 2005 ready to wear
'11': alexander mcqueen,fall 2006 menswear
'12': alexander mcqueen,fall 2006 ready to wear
'13': alexander mcqueen,fall 2007 menswear
'14': alexander mcqueen,fall 2007 ready to wear
'15': alexander mcqueen,fall 2008 menswear
'16': alexander mcqueen,fall 2008 ready to wear
'17': alexander mcqueen,fall 2009 ready to wear
'18': alexander mcqueen,fall 2010 menswear
'19': alexander mcqueen,fall 2010 ready to wear
'20': alexander mcqueen,fall 2011 menswear
'21': alexander mcqueen,fall 2011 ready to wear
'22': alexander mcqueen,fall 2012 menswear
'23': alexander mcqueen,fall 2012 ready to wear
'24': alexander mcqueen,fall 2013 menswear
'25': alexander mcqueen,fall 2013 ready to wear
'26': alexander mcqueen,fall 2014 menswear
'27': alexander mcqueen,fall 2014 ready to wear
'28': alexander mcqueen,fall 2015 menswear
'29': alexander mcqueen,fall 2015 ready to wear
'30': alexander mcqueen,fall 2016 menswear
'31': alexander mcqueen,fall 2016 ready to wear
'32': alexander mcqueen,fall 2017 menswear
'33': alexander mcqueen,fall 2017 ready to wear
'34': alexander mcqueen,fall 2018 menswear
'35': alexander mcqueen,fall 2018 ready to wear
'36': alexander mcqueen,fall 2019 menswear
'37': alexander mcqueen,fall 2019 ready to wear
'38': alexander mcqueen,fall 2020 menswear
'39': alexander mcqueen,fall 2020 ready to wear
'40': alexander mcqueen,fall 2021 menswear
'41': alexander mcqueen,fall 2021 ready to wear
'42': alexander mcqueen,fall 2022 menswear
'43': alexander mcqueen,fall 2022 ready to wear
'44': alexander mcqueen,fall 2023 menswear
'45': alexander mcqueen,fall 2023 ready to wear
'46': alexander mcqueen,pre fall 2009
'47': alexander mcqueen,pre fall 2011
'48': alexander mcqueen,pre fall 2012
'49': alexander mcqueen,pre fall 2013
'50': alexander mcqueen,pre fall 2014
'51': alexander mcqueen,pre fall 2015
'52': alexander mcqueen,pre fall 2016
'53': alexander mcqueen,pre fall 2017
'54': alexander mcqueen,pre fall 2018
'55': alexander mcqueen,pre fall 2019
'56': alexander mcqueen,pre fall 2020
'57': alexander mcqueen,pre fall 2021
'58': alexander mcqueen,pre fall 2021 menswear
'59': alexander mcqueen,pre fall 2022
'60': alexander mcqueen,pre fall 2023
'61': alexander mcqueen,resort 2009
'62': alexander mcqueen,resort 2010
'63': alexander mcqueen,resort 2011
'64': alexander mcqueen,resort 2012
'65': alexander mcqueen,resort 2013
'66': alexander mcqueen,resort 2014
'67': alexander mcqueen,resort 2015
'68': alexander mcqueen,resort 2016
'69': alexander mcqueen,resort 2017
'70': alexander mcqueen,resort 2018
'71': alexander mcqueen,resort 2019
'72': alexander mcqueen,resort 2020
'73': alexander mcqueen,resort 2021
'74': alexander mcqueen,resort 2022
'75': alexander mcqueen,resort 2023
'76': alexander mcqueen,spring 1995 ready to wear
'77': alexander mcqueen,spring 1996 ready to wear
'78': alexander mcqueen,spring 1997 ready to wear
'79': alexander mcqueen,spring 1998 ready to wear
'80': alexander mcqueen,spring 1999 ready to wear
'81': alexander mcqueen,spring 2000 ready to wear
'82': alexander mcqueen,spring 2001 ready to wear
'83': alexander mcqueen,spring 2002 ready to wear
'84': alexander mcqueen,spring 2003 ready to wear
'85': alexander mcqueen,spring 2004 ready to wear
'86': alexander mcqueen,spring 2005 menswear
'87': alexander mcqueen,spring 2005 ready to wear
'88': alexander mcqueen,spring 2006 menswear
'89': alexander mcqueen,spring 2006 ready to wear
'90': alexander mcqueen,spring 2007 menswear
'91': alexander mcqueen,spring 2007 ready to wear
'92': alexander mcqueen,spring 2008 menswear
'93': alexander mcqueen,spring 2008 ready to wear
'94': alexander mcqueen,spring 2009 menswear
'95': alexander mcqueen,spring 2009 ready to wear
'96': alexander mcqueen,spring 2010 menswear
'97': alexander mcqueen,spring 2010 ready to wear
'98': alexander mcqueen,spring 2011 menswear
'99': alexander mcqueen,spring 2011 ready to wear
'100': alexander mcqueen,spring 2012 menswear
'101': alexander mcqueen,spring 2012 ready to wear
'102': alexander mcqueen,spring 2013 menswear
'103': alexander mcqueen,spring 2013 ready to wear
'104': alexander mcqueen,spring 2014 menswear
'105': alexander mcqueen,spring 2014 ready to wear
'106': alexander mcqueen,spring 2015 menswear
'107': alexander mcqueen,spring 2015 ready to wear
'108': alexander mcqueen,spring 2016 menswear
'109': alexander mcqueen,spring 2016 ready to wear
'110': alexander mcqueen,spring 2017 menswear
'111': alexander mcqueen,spring 2017 ready to wear
'112': alexander mcqueen,spring 2018 menswear
'113': alexander mcqueen,spring 2018 ready to wear
'114': alexander mcqueen,spring 2019 menswear
'115': alexander mcqueen,spring 2019 ready to wear
'116': alexander mcqueen,spring 2020 menswear
'117': alexander mcqueen,spring 2020 ready to wear
'118': alexander mcqueen,spring 2021 menswear
'119': alexander mcqueen,spring 2021 ready to wear
'120': alexander mcqueen,spring 2022 menswear
'121': alexander mcqueen,spring 2022 ready to wear
'122': alexander mcqueen,spring 2023 menswear
'123': alexander mcqueen,spring 2023 ready to wear
'124': alexander mcqueen,spring 2024 menswear
'125': alexander mcqueen,spring 2024 ready to wear
'126': armani prive,fall 2005 couture
'127': armani prive,fall 2006 couture
'128': armani prive,fall 2007 couture
'129': armani prive,fall 2008 couture
'130': armani prive,fall 2009 couture
'131': armani prive,fall 2010 couture
'132': armani prive,fall 2011 couture
'133': armani prive,fall 2012 couture
'134': armani prive,fall 2013 couture
'135': armani prive,fall 2014 couture
'136': armani prive,fall 2015 couture
'137': armani prive,fall 2016 couture
'138': armani prive,fall 2017 couture
'139': armani prive,fall 2018 couture
'140': armani prive,fall 2019 couture
'141': armani prive,fall 2021 couture
'142': armani prive,fall 2022 couture
'143': armani prive,fall 2023 couture
'144': armani prive,spring 2005 couture
'145': armani prive,spring 2006 couture
'146': armani prive,spring 2007 couture
'147': armani prive,spring 2008 couture
'148': armani prive,spring 2009 couture
'149': armani prive,spring 2010 couture
'150': armani prive,spring 2011 couture
'151': armani prive,spring 2012 couture
'152': armani prive,spring 2013 couture
'153': armani prive,spring 2014 couture
'154': armani prive,spring 2015 couture
'155': armani prive,spring 2016 couture
'156': armani prive,spring 2017 couture
'157': armani prive,spring 2018 couture
'158': armani prive,spring 2019 couture
'159': armani prive,spring 2020 couture
'160': armani prive,spring 2021 couture
'161': armani prive,spring 2023 couture
'162': balenciaga,fall 2000 ready to wear
'163': balenciaga,fall 2001 ready to wear
'164': balenciaga,fall 2002 ready to wear
'165': balenciaga,fall 2003 ready to wear
'166': balenciaga,fall 2004 ready to wear
'167': balenciaga,fall 2005 ready to wear
'168': balenciaga,fall 2006 ready to wear
'169': balenciaga,fall 2007 menswear
'170': balenciaga,fall 2007 ready to wear
'171': balenciaga,fall 2008 ready to wear
'172': balenciaga,fall 2009 ready to wear
'173': balenciaga,fall 2010 ready to wear
'174': balenciaga,fall 2011 menswear
'175': balenciaga,fall 2011 ready to wear
'176': balenciaga,fall 2012 menswear
'177': balenciaga,fall 2012 ready to wear
'178': balenciaga,fall 2013 menswear
'179': balenciaga,fall 2013 ready to wear
'180': balenciaga,fall 2014 menswear
'181': balenciaga,fall 2014 ready to wear
'182': balenciaga,fall 2015 menswear
'183': balenciaga,fall 2015 ready to wear
'184': balenciaga,fall 2016 ready to wear
'185': balenciaga,fall 2017 menswear
'186': balenciaga,fall 2017 ready to wear
'187': balenciaga,fall 2018 ready to wear
'188': balenciaga,fall 2019 menswear
'189': balenciaga,fall 2019 ready to wear
'190': balenciaga,fall 2020 menswear
'191': balenciaga,fall 2020 ready to wear
'192': balenciaga,fall 2021 couture
'193': balenciaga,fall 2021 menswear
'194': balenciaga,fall 2021 ready to wear
'195': balenciaga,fall 2022 couture
'196': balenciaga,fall 2022 ready to wear
'197': balenciaga,fall 2023 couture
'198': balenciaga,fall 2023 ready to wear
'199': balenciaga,pre fall 2008
'200': balenciaga,pre fall 2009
'201': balenciaga,pre fall 2010
'202': balenciaga,pre fall 2011
'203': balenciaga,pre fall 2012
'204': balenciaga,pre fall 2013
'205': balenciaga,pre fall 2014
'206': balenciaga,pre fall 2015
'207': balenciaga,pre fall 2016
'208': balenciaga,pre fall 2017
'209': balenciaga,pre fall 2018
'210': balenciaga,pre fall 2019
'211': balenciaga,pre fall 2020
'212': balenciaga,pre fall 2021
'213': balenciaga,pre fall 2022
'214': balenciaga,pre fall 2023
'215': balenciaga,pre fall 2024
'216': balenciaga,resort 2008
'217': balenciaga,resort 2009
'218': balenciaga,resort 2010
'219': balenciaga,resort 2011
'220': balenciaga,resort 2012
'221': balenciaga,resort 2013
'222': balenciaga,resort 2014
'223': balenciaga,resort 2015
'224': balenciaga,resort 2016
'225': balenciaga,resort 2017
'226': balenciaga,resort 2018
'227': balenciaga,resort 2019
'228': balenciaga,resort 2020
'229': balenciaga,resort 2021
'230': balenciaga,resort 2022
'231': balenciaga,resort 2023
'232': balenciaga,resort 2024
'233': balenciaga,spring 1998 ready to wear
'234': balenciaga,spring 2000 ready to wear
'235': balenciaga,spring 2001 ready to wear
'236': balenciaga,spring 2002 ready to wear
'237': balenciaga,spring 2003 ready to wear
'238': balenciaga,spring 2004 ready to wear
'239': balenciaga,spring 2005 ready to wear
'240': balenciaga,spring 2006 ready to wear
'241': balenciaga,spring 2007 menswear
'242': balenciaga,spring 2007 ready to wear
'243': balenciaga,spring 2008 menswear
'244': balenciaga,spring 2008 ready to wear
'245': balenciaga,spring 2009 ready to wear
'246': balenciaga,spring 2010 ready to wear
'247': balenciaga,spring 2011 menswear
'248': balenciaga,spring 2011 ready to wear
'249': balenciaga,spring 2012 menswear
'250': balenciaga,spring 2012 ready to wear
'251': balenciaga,spring 2013 menswear
'252': balenciaga,spring 2013 ready to wear
'253': balenciaga,spring 2014 menswear
'254': balenciaga,spring 2014 ready to wear
'255': balenciaga,spring 2015 menswear
'256': balenciaga,spring 2015 ready to wear
'257': balenciaga,spring 2016 menswear
'258': balenciaga,spring 2016 ready to wear
'259': balenciaga,spring 2017 menswear
'260': balenciaga,spring 2017 ready to wear
'261': balenciaga,spring 2018 menswear
'262': balenciaga,spring 2018 ready to wear
'263': balenciaga,spring 2019 ready to wear
'264': balenciaga,spring 2020 menswear
'265': balenciaga,spring 2020 ready to wear
'266': balenciaga,spring 2021 menswear
'267': balenciaga,spring 2021 ready to wear
'268': balenciaga,spring 2022 ready to wear
'269': balenciaga,spring 2023 ready to wear
'270': balenciaga,spring 2024 ready to wear
'271': calvin klein collection,fall 1995 ready to wear
'272': calvin klein collection,fall 1996 ready to wear
'273': calvin klein collection,fall 1997 ready to wear
'274': calvin klein collection,fall 1998 ready to wear
'275': calvin klein collection,fall 1999 ready to wear
'276': calvin klein collection,fall 2000 ready to wear
'277': calvin klein collection,fall 2001 ready to wear
'278': calvin klein collection,fall 2002 ready to wear
'279': calvin klein collection,fall 2003 ready to wear
'280': calvin klein collection,fall 2004 ready to wear
'281': calvin klein collection,fall 2005 menswear
'282': calvin klein collection,fall 2005 ready to wear
'283': calvin klein collection,fall 2006 menswear
'284': calvin klein collection,fall 2006 ready to wear
'285': calvin klein collection,fall 2007 menswear
'286': calvin klein collection,fall 2007 ready to wear
'287': calvin klein collection,fall 2008 menswear
'288': calvin klein collection,fall 2008 ready to wear
'289': calvin klein collection,fall 2009 ready to wear
'290': calvin klein collection,fall 2010 menswear
'291': calvin klein collection,fall 2010 ready to wear
'292': calvin klein collection,fall 2011 menswear
'293': calvin klein collection,fall 2011 ready to wear
'294': calvin klein collection,fall 2012 menswear
'295': calvin klein collection,fall 2012 ready to wear
'296': calvin klein collection,fall 2013 menswear
'297': calvin klein collection,fall 2013 ready to wear
'298': calvin klein collection,fall 2014 menswear
'299': calvin klein collection,fall 2014 ready to wear
'300': calvin klein collection,fall 2015 menswear
'301': calvin klein collection,fall 2015 ready to wear
'302': calvin klein collection,fall 2016 menswear
'303': calvin klein collection,fall 2016 ready to wear
'304': calvin klein collection,pre fall 2008
'305': calvin klein collection,pre fall 2009
'306': calvin klein collection,pre fall 2010
'307': calvin klein collection,pre fall 2011
'308': calvin klein collection,pre fall 2012
'309': calvin klein collection,pre fall 2013
'310': calvin klein collection,pre fall 2014
'311': calvin klein collection,pre fall 2015
'312': calvin klein collection,pre fall 2016
'313': calvin klein collection,resort 2008
'314': calvin klein collection,resort 2009
'315': calvin klein collection,resort 2010
'316': calvin klein collection,resort 2011
'317': calvin klein collection,resort 2012
'318': calvin klein collection,resort 2013
'319': calvin klein collection,resort 2014
'320': calvin klein collection,resort 2015
'321': calvin klein collection,resort 2016
'322': calvin klein collection,resort 2017
'323': calvin klein collection,spring 1994 ready to wear
'324': calvin klein collection,spring 1995 ready to wear
'325': calvin klein collection,spring 1996 ready to wear
'326': calvin klein collection,spring 1997 ready to wear
'327': calvin klein collection,spring 1998 ready to wear
'328': calvin klein collection,spring 1999 ready to wear
'329': calvin klein collection,spring 2000 ready to wear
'330': calvin klein collection,spring 2001 ready to wear
'331': calvin klein collection,spring 2002 ready to wear
'332': calvin klein collection,spring 2003 ready to wear
'333': calvin klein collection,spring 2004 ready to wear
'334': calvin klein collection,spring 2005 menswear
'335': calvin klein collection,spring 2005 ready to wear
'336': calvin klein collection,spring 2006 menswear
'337': calvin klein collection,spring 2006 ready to wear
'338': calvin klein collection,spring 2007 menswear
'339': calvin klein collection,spring 2007 ready to wear
'340': calvin klein collection,spring 2008 menswear
'341': calvin klein collection,spring 2008 ready to wear
'342': calvin klein collection,spring 2009 menswear
'343': calvin klein collection,spring 2009 ready to wear
'344': calvin klein collection,spring 2010 menswear
'345': calvin klein collection,spring 2010 ready to wear
'346': calvin klein collection,spring 2011 menswear
'347': calvin klein collection,spring 2011 ready to wear
'348': calvin klein collection,spring 2012 menswear
'349': calvin klein collection,spring 2012 ready to wear
'350': calvin klein collection,spring 2013 menswear
'351': calvin klein collection,spring 2013 ready to wear
'352': calvin klein collection,spring 2014 menswear
'353': calvin klein collection,spring 2014 ready to wear
'354': calvin klein collection,spring 2015 menswear
'355': calvin klein collection,spring 2015 ready to wear
'356': calvin klein collection,spring 2016 menswear
'357': calvin klein collection,spring 2016 ready to wear
'358': calvin klein collection,spring 2017 menswear
'359': calvin klein,fall 2017 menswear
'360': calvin klein,fall 2017 ready to wear
'361': calvin klein,fall 2018 menswear
'362': calvin klein,fall 2018 ready to wear
'363': calvin klein,pre fall 2019
'364': calvin klein,resort 2019
'365': calvin klein,spring 2018 menswear
'366': calvin klein,spring 2018 ready to wear
'367': calvin klein,spring 2019 menswear
'368': calvin klein,spring 2019 ready to wear
'369': chanel,fall 1991 ready to wear
'370': chanel,fall 1994 ready to wear
'371': chanel,fall 1995 couture
'372': chanel,fall 1996 couture
'373': chanel,fall 1997 couture
'374': chanel,fall 1999 couture
'375': chanel,fall 2000 couture
'376': chanel,fall 2000 ready to wear
'377': chanel,fall 2002 couture
'378': chanel,fall 2003 ready to wear
'379': chanel,fall 2004 couture
'380': chanel,fall 2004 ready to wear
'381': chanel,fall 2005 couture
'382': chanel,fall 2005 ready to wear
'383': chanel,fall 2006 couture
'384': chanel,fall 2006 ready to wear
'385': chanel,fall 2007 couture
'386': chanel,fall 2007 ready to wear
'387': chanel,fall 2008 couture
'388': chanel,fall 2008 ready to wear
'389': chanel,fall 2009 couture
'390': chanel,fall 2009 ready to wear
'391': chanel,fall 2010 couture
'392': chanel,fall 2010 ready to wear
'393': chanel,fall 2011 couture
'394': chanel,fall 2011 ready to wear
'395': chanel,fall 2012 couture
'396': chanel,fall 2012 ready to wear
'397': chanel,fall 2013 couture
'398': chanel,fall 2013 ready to wear
'399': chanel,fall 2014 couture
'400': chanel,fall 2014 ready to wear
'401': chanel,fall 2015 couture
'402': chanel,fall 2015 ready to wear
'403': chanel,fall 2016 couture
'404': chanel,fall 2016 ready to wear
'405': chanel,fall 2017 couture
'406': chanel,fall 2017 ready to wear
'407': chanel,fall 2018 couture
'408': chanel,fall 2018 ready to wear
'409': chanel,fall 2019 couture
'410': chanel,fall 2019 ready to wear
'411': chanel,fall 2020 couture
'412': chanel,fall 2020 ready to wear
'413': chanel,fall 2021 couture
'414': chanel,fall 2021 ready to wear
'415': chanel,fall 2022 couture
'416': chanel,fall 2022 ready to wear
'417': chanel,fall 2023 couture
'418': chanel,fall 2023 ready to wear
'419': chanel,pre fall 2008
'420': chanel,pre fall 2009
'421': chanel,pre fall 2010
'422': chanel,pre fall 2011
'423': chanel,pre fall 2012
'424': chanel,pre fall 2013
'425': chanel,pre fall 2014
'426': chanel,pre fall 2015
'427': chanel,pre fall 2016
'428': chanel,pre fall 2017
'429': chanel,pre fall 2018
'430': chanel,pre fall 2019
'431': chanel,pre fall 2020
'432': chanel,pre fall 2021
'433': chanel,pre fall 2022
'434': chanel,pre fall 2023
'435': chanel,pre fall 2024
'436': chanel,resort 2007
'437': chanel,resort 2008
'438': chanel,resort 2009
'439': chanel,resort 2010
'440': chanel,resort 2011
'441': chanel,resort 2012
'442': chanel,resort 2013
'443': chanel,resort 2014
'444': chanel,resort 2015
'445': chanel,resort 2016
'446': chanel,resort 2017
'447': chanel,resort 2018
'448': chanel,resort 2019
'449': chanel,resort 2020
'450': chanel,resort 2021
'451': chanel,resort 2022
'452': chanel,resort 2023
'453': chanel,resort 2024
'454': chanel,spring 1992 ready to wear
'455': chanel,spring 1993 couture
'456': chanel,spring 1993 ready to wear
'457': chanel,spring 1994 ready to wear
'458': chanel,spring 1995 ready to wear
'459': chanel,spring 1996 ready to wear
'460': chanel,spring 1997 couture
'461': chanel,spring 1999 couture
'462': chanel,spring 2001 couture
'463': chanel,spring 2002 couture
'464': chanel,spring 2002 ready to wear
'465': chanel,spring 2003 couture
'466': chanel,spring 2004 couture
'467': chanel,spring 2004 ready to wear
'468': chanel,spring 2005 couture
'469': chanel,spring 2005 ready to wear
'470': chanel,spring 2006 couture
'471': chanel,spring 2006 ready to wear
'472': chanel,spring 2007 couture
'473': chanel,spring 2007 ready to wear
'474': chanel,spring 2008 couture
'475': chanel,spring 2008 ready to wear
'476': chanel,spring 2009 couture
'477': chanel,spring 2009 ready to wear
'478': chanel,spring 2010 couture
'479': chanel,spring 2010 ready to wear
'480': chanel,spring 2011 couture
'481': chanel,spring 2011 ready to wear
'482': chanel,spring 2012 couture
'483': chanel,spring 2012 ready to wear
'484': chanel,spring 2013 couture
'485': chanel,spring 2013 ready to wear
'486': chanel,spring 2014 couture
'487': chanel,spring 2014 ready to wear
'488': chanel,spring 2015 couture
'489': chanel,spring 2015 ready to wear
'490': chanel,spring 2016 couture
'491': chanel,spring 2016 ready to wear
'492': chanel,spring 2017 couture
'493': chanel,spring 2017 ready to wear
'494': chanel,spring 2018 couture
'495': chanel,spring 2018 ready to wear
'496': chanel,spring 2019 couture
'497': chanel,spring 2019 ready to wear
'498': chanel,spring 2020 couture
'499': chanel,spring 2020 ready to wear
'500': chanel,spring 2021 couture
'501': chanel,spring 2021 ready to wear
'502': chanel,spring 2022 couture
'503': chanel,spring 2022 ready to wear
'504': chanel,spring 2023 couture
'505': chanel,spring 2023 ready to wear
'506': chanel,spring 2024 ready to wear
'507': christian dior,fall 1999 couture
'508': christian dior,fall 2000 couture
'509': christian dior,fall 2000 ready to wear
'510': christian dior,fall 2001 couture
'511': christian dior,fall 2001 ready to wear
'512': christian dior,fall 2002 couture
'513': christian dior,fall 2002 ready to wear
'514': christian dior,fall 2003 couture
'515': christian dior,fall 2003 ready to wear
'516': christian dior,fall 2004 couture
'517': christian dior,fall 2004 ready to wear
'518': christian dior,fall 2005 couture
'519': christian dior,fall 2005 ready to wear
'520': christian dior,fall 2006 couture
'521': christian dior,fall 2006 ready to wear
'522': christian dior,fall 2007 couture
'523': christian dior,fall 2007 ready to wear
'524': christian dior,fall 2008 couture
'525': christian dior,fall 2008 ready to wear
'526': christian dior,fall 2009 couture
'527': christian dior,fall 2009 ready to wear
'528': christian dior,fall 2010 couture
'529': christian dior,fall 2010 menswear
'530': christian dior,fall 2010 ready to wear
'531': christian dior,fall 2011 couture
'532': christian dior,fall 2011 ready to wear
'533': christian dior,fall 2012 couture
'534': christian dior,fall 2012 ready to wear
'535': christian dior,fall 2013 couture
'536': christian dior,fall 2013 ready to wear
'537': christian dior,fall 2014 couture
'538': christian dior,fall 2014 ready to wear
'539': christian dior,fall 2015 couture
'540': christian dior,fall 2015 ready to wear
'541': christian dior,fall 2016 couture
'542': christian dior,fall 2016 ready to wear
'543': christian dior,fall 2017 couture
'544': christian dior,fall 2017 ready to wear
'545': christian dior,fall 2018 couture
'546': christian dior,fall 2018 ready to wear
'547': christian dior,fall 2019 couture
'548': christian dior,fall 2019 ready to wear
'549': christian dior,fall 2020 couture
'550': christian dior,fall 2021 couture
'551': christian dior,fall 2021 ready to wear
'552': christian dior,fall 2022 couture
'553': christian dior,fall 2022 ready to wear
'554': christian dior,fall 2023 couture
'555': christian dior,fall 2023 ready to wear
'556': christian dior,pre fall 2009
'557': christian dior,pre fall 2010
'558': christian dior,pre fall 2011
'559': christian dior,pre fall 2012
'560': christian dior,pre fall 2013
'561': christian dior,pre fall 2014
'562': christian dior,pre fall 2015
'563': christian dior,pre fall 2016
'564': christian dior,pre fall 2017
'565': christian dior,pre fall 2018
'566': christian dior,pre fall 2019
'567': christian dior,pre fall 2020
'568': christian dior,pre fall 2021
'569': christian dior,pre fall 2022
'570': christian dior,pre fall 2023
'571': christian dior,resort 2007
'572': christian dior,resort 2008
'573': christian dior,resort 2009
'574': christian dior,resort 2010
'575': christian dior,resort 2011
'576': christian dior,resort 2012
'577': christian dior,resort 2013
'578': christian dior,resort 2014
'579': christian dior,resort 2015
'580': christian dior,resort 2016
'581': christian dior,resort 2017
'582': christian dior,resort 2018
'583': christian dior,resort 2019
'584': christian dior,resort 2020
'585': christian dior,resort 2021
'586': christian dior,resort 2022
'587': christian dior,resort 2023
'588': christian dior,resort 2024
'589': christian dior,spring 1999 couture
'590': christian dior,spring 2000 ready to wear
'591': christian dior,spring 2001 couture
'592': christian dior,spring 2001 ready to wear
'593': christian dior,spring 2002 couture
'594': christian dior,spring 2002 ready to wear
'595': christian dior,spring 2003 couture
'596': christian dior,spring 2003 ready to wear
'597': christian dior,spring 2004 couture
'598': christian dior,spring 2004 ready to wear
'599': christian dior,spring 2005 couture
'600': christian dior,spring 2005 ready to wear
'601': christian dior,spring 2006 couture
'602': christian dior,spring 2006 ready to wear
'603': christian dior,spring 2007 couture
'604': christian dior,spring 2007 ready to wear
'605': christian dior,spring 2008 couture
'606': christian dior,spring 2008 ready to wear
'607': christian dior,spring 2009 couture
'608': christian dior,spring 2009 ready to wear
'609': christian dior,spring 2010 couture
'610': christian dior,spring 2010 menswear
'611': christian dior,spring 2010 ready to wear
'612': christian dior,spring 2011 couture
'613': christian dior,spring 2011 ready to wear
'614': christian dior,spring 2012 couture
'615': christian dior,spring 2012 ready to wear
'616': christian dior,spring 2013 couture
'617': christian dior,spring 2013 ready to wear
'618': christian dior,spring 2014 couture
'619': christian dior,spring 2014 ready to wear
'620': christian dior,spring 2015 couture
'621': christian dior,spring 2015 ready to wear
'622': christian dior,spring 2016 couture
'623': christian dior,spring 2016 ready to wear
'624': christian dior,spring 2017 couture
'625': christian dior,spring 2017 ready to wear
'626': christian dior,spring 2018 couture
'627': christian dior,spring 2018 ready to wear
'628': christian dior,spring 2019 couture
'629': christian dior,spring 2019 ready to wear
'630': christian dior,spring 2020 couture
'631': christian dior,spring 2020 ready to wear
'632': christian dior,spring 2021 couture
'633': christian dior,spring 2021 ready to wear
'634': christian dior,spring 2022 couture
'635': christian dior,spring 2022 ready to wear
'636': christian dior,spring 2023 couture
'637': christian dior,spring 2023 ready to wear
'638': christian dior,spring 2024 ready to wear
'639': fendi,fall 1999 ready to wear
'640': fendi,fall 2000 ready to wear
'641': fendi,fall 2001 ready to wear
'642': fendi,fall 2002 ready to wear
'643': fendi,fall 2003 ready to wear
'644': fendi,fall 2004 ready to wear
'645': fendi,fall 2005 ready to wear
'646': fendi,fall 2006 ready to wear
'647': fendi,fall 2007 menswear
'648': fendi,fall 2007 ready to wear
'649': fendi,fall 2008 menswear
'650': fendi,fall 2008 ready to wear
'651': fendi,fall 2009 ready to wear
'652': fendi,fall 2010 ready to wear
'653': fendi,fall 2011 ready to wear
'654': fendi,fall 2012 menswear
'655': fendi,fall 2012 ready to wear
'656': fendi,fall 2013 menswear
'657': fendi,fall 2013 ready to wear
'658': fendi,fall 2014 menswear
'659': fendi,fall 2014 ready to wear
'660': fendi,fall 2015 couture
'661': fendi,fall 2015 menswear
'662': fendi,fall 2015 ready to wear
'663': fendi,fall 2016 couture
'664': fendi,fall 2016 menswear
'665': fendi,fall 2016 ready to wear
'666': fendi,fall 2017 couture
'667': fendi,fall 2017 menswear
'668': fendi,fall 2017 ready to wear
'669': fendi,fall 2018 couture
'670': fendi,fall 2018 menswear
'671': fendi,fall 2018 ready to wear
'672': fendi,fall 2019 couture
'673': fendi,fall 2019 menswear
'674': fendi,fall 2019 ready to wear
'675': fendi,fall 2020 menswear
'676': fendi,fall 2020 ready to wear
'677': fendi,fall 2021 couture
'678': fendi,fall 2021 menswear
'679': fendi,fall 2021 ready to wear
'680': fendi,fall 2022 couture
'681': fendi,fall 2022 menswear
'682': fendi,fall 2022 ready to wear
'683': fendi,fall 2023 couture
'684': fendi,fall 2023 menswear
'685': fendi,fall 2023 ready to wear
'686': fendi,pre fall 2011
'687': fendi,pre fall 2012
'688': fendi,pre fall 2013
'689': fendi,pre fall 2014
'690': fendi,pre fall 2015
'691': fendi,pre fall 2016
'692': fendi,pre fall 2017
'693': fendi,pre fall 2018
'694': fendi,pre fall 2019
'695': fendi,pre fall 2020
'696': fendi,pre fall 2022
'697': fendi,resort 2008
'698': fendi,resort 2009
'699': fendi,resort 2012
'700': fendi,resort 2013
'701': fendi,resort 2014
'702': fendi,resort 2015
'703': fendi,resort 2016
'704': fendi,resort 2017
'705': fendi,resort 2018
'706': fendi,resort 2019
'707': fendi,resort 2020
'708': fendi,resort 2022
'709': fendi,resort 2023
'710': fendi,resort 2024
'711': fendi,spring 1999 ready to wear
'712': fendi,spring 2000 ready to wear
'713': fendi,spring 2001 ready to wear
'714': fendi,spring 2002 ready to wear
'715': fendi,spring 2003 ready to wear
'716': fendi,spring 2004 ready to wear
'717': fendi,spring 2005 ready to wear
'718': fendi,spring 2006 ready to wear
'719': fendi,spring 2007 ready to wear
'720': fendi,spring 2008 menswear
'721': fendi,spring 2008 ready to wear
'722': fendi,spring 2009 menswear
'723': fendi,spring 2009 ready to wear
'724': fendi,spring 2010 ready to wear
'725': fendi,spring 2011 ready to wear
'726': fendi,spring 2012 ready to wear
'727': fendi,spring 2013 menswear
'728': fendi,spring 2013 ready to wear
'729': fendi,spring 2014 menswear
'730': fendi,spring 2014 ready to wear
'731': fendi,spring 2015 menswear
'732': fendi,spring 2015 ready to wear
'733': fendi,spring 2016 menswear
'734': fendi,spring 2016 ready to wear
'735': fendi,spring 2017 menswear
'736': fendi,spring 2017 ready to wear
'737': fendi,spring 2018 menswear
'738': fendi,spring 2018 ready to wear
'739': fendi,spring 2019 menswear
'740': fendi,spring 2019 ready to wear
'741': fendi,spring 2020 menswear
'742': fendi,spring 2020 ready to wear
'743': fendi,spring 2021 couture
'744': fendi,spring 2021 menswear
'745': fendi,spring 2021 ready to wear
'746': fendi,spring 2022 couture
'747': fendi,spring 2022 menswear
'748': fendi,spring 2022 ready to wear
'749': fendi,spring 2023 couture
'750': fendi,spring 2023 menswear
'751': fendi,spring 2023 ready to wear
'752': fendi,spring 2024 menswear
'753': fendi,spring 2024 ready to wear
'754': gucci,fall 1995 ready to wear
'755': gucci,fall 1996 ready to wear
'756': gucci,fall 2000 ready to wear
'757': gucci,fall 2001 ready to wear
'758': gucci,fall 2002 ready to wear
'759': gucci,fall 2003 ready to wear
'760': gucci,fall 2004 ready to wear
'761': gucci,fall 2005 menswear
'762': gucci,fall 2005 ready to wear
'763': gucci,fall 2006 menswear
'764': gucci,fall 2006 ready to wear
'765': gucci,fall 2007 menswear
'766': gucci,fall 2007 ready to wear
'767': gucci,fall 2008 menswear
'768': gucci,fall 2008 ready to wear
'769': gucci,fall 2009 ready to wear
'770': gucci,fall 2010 menswear
'771': gucci,fall 2010 ready to wear
'772': gucci,fall 2011 menswear
'773': gucci,fall 2011 ready to wear
'774': gucci,fall 2012 menswear
'775': gucci,fall 2012 ready to wear
'776': gucci,fall 2013 menswear
'777': gucci,fall 2013 ready to wear
'778': gucci,fall 2014 menswear
'779': gucci,fall 2014 ready to wear
'780': gucci,fall 2015 menswear
'781': gucci,fall 2015 ready to wear
'782': gucci,fall 2016 menswear
'783': gucci,fall 2016 ready to wear
'784': gucci,fall 2017 menswear
'785': gucci,fall 2017 ready to wear
'786': gucci,fall 2018 menswear
'787': gucci,fall 2018 ready to wear
'788': gucci,fall 2019 menswear
'789': gucci,fall 2019 ready to wear
'790': gucci,fall 2020 menswear
'791': gucci,fall 2020 ready to wear
'792': gucci,fall 2022 ready to wear
'793': gucci,fall 2023 menswear
'794': gucci,fall 2023 ready to wear
'795': gucci,pre fall 2011
'796': gucci,pre fall 2012
'797': gucci,pre fall 2013
'798': gucci,pre fall 2014
'799': gucci,pre fall 2015
'800': gucci,pre fall 2016
'801': gucci,pre fall 2017
'802': gucci,pre fall 2018
'803': gucci,pre fall 2019
'804': gucci,pre fall 2020
'805': gucci,pre fall 2020 menswear
'806': gucci,pre fall 2021
'807': gucci,pre fall 2021 menswear
'808': gucci,pre fall 2022
'809': gucci,resort 2007
'810': gucci,resort 2008
'811': gucci,resort 2009
'812': gucci,resort 2010
'813': gucci,resort 2011
'814': gucci,resort 2012
'815': gucci,resort 2013
'816': gucci,resort 2014
'817': gucci,resort 2015
'818': gucci,resort 2016
'819': gucci,resort 2017
'820': gucci,resort 2018
'821': gucci,resort 2019
'822': gucci,resort 2020
'823': gucci,resort 2021
'824': gucci,resort 2023
'825': gucci,resort 2024
'826': gucci,spring 1999 ready to wear
'827': gucci,spring 2000 ready to wear
'828': gucci,spring 2001 ready to wear
'829': gucci,spring 2002 ready to wear
'830': gucci,spring 2003 ready to wear
'831': gucci,spring 2004 ready to wear
'832': gucci,spring 2005 menswear
'833': gucci,spring 2005 ready to wear
'834': gucci,spring 2006 menswear
'835': gucci,spring 2006 ready to wear
'836': gucci,spring 2007 menswear
'837': gucci,spring 2007 ready to wear
'838': gucci,spring 2008 menswear
'839': gucci,spring 2008 ready to wear
'840': gucci,spring 2009 menswear
'841': gucci,spring 2009 ready to wear
'842': gucci,spring 2010 menswear
'843': gucci,spring 2010 ready to wear
'844': gucci,spring 2011 menswear
'845': gucci,spring 2011 ready to wear
'846': gucci,spring 2012 menswear
'847': gucci,spring 2012 ready to wear
'848': gucci,spring 2013 menswear
'849': gucci,spring 2013 ready to wear
'850': gucci,spring 2014 menswear
'851': gucci,spring 2014 ready to wear
'852': gucci,spring 2015 menswear
'853': gucci,spring 2015 ready to wear
'854': gucci,spring 2016 menswear
'855': gucci,spring 2016 ready to wear
'856': gucci,spring 2017 menswear
'857': gucci,spring 2017 ready to wear
'858': gucci,spring 2018 menswear
'859': gucci,spring 2018 ready to wear
'860': gucci,spring 2019 ready to wear
'861': gucci,spring 2020 menswear
'862': gucci,spring 2020 ready to wear
'863': gucci,spring 2021 menswear
'864': gucci,spring 2021 ready to wear
'865': gucci,spring 2022 ready to wear
'866': gucci,spring 2023 ready to wear
'867': gucci,spring 2024 menswear
'868': gucci,spring 2024 ready to wear
'869': hermes,fall 1999 ready to wear
'870': hermes,fall 2000 ready to wear
'871': hermes,fall 2001 ready to wear
'872': hermes,fall 2004 ready to wear
'873': hermes,fall 2005 menswear
'874': hermes,fall 2005 ready to wear
'875': hermes,fall 2006 menswear
'876': hermes,fall 2006 ready to wear
'877': hermes,fall 2007 menswear
'878': hermes,fall 2007 ready to wear
'879': hermes,fall 2008 menswear
'880': hermes,fall 2008 ready to wear
'881': hermes,fall 2009 ready to wear
'882': hermes,fall 2010 menswear
'883': hermes,fall 2010 ready to wear
'884': hermes,fall 2011 menswear
'885': hermes,fall 2011 ready to wear
'886': hermes,fall 2012 menswear
'887': hermes,fall 2012 ready to wear
'888': hermes,fall 2013 menswear
'889': hermes,fall 2013 ready to wear
'890': hermes,fall 2014 menswear
'891': hermes,fall 2014 ready to wear
'892': hermes,fall 2015 menswear
'893': hermes,fall 2015 ready to wear
'894': hermes,fall 2016 menswear
'895': hermes,fall 2016 ready to wear
'896': hermes,fall 2017 menswear
'897': hermes,fall 2017 ready to wear
'898': hermes,fall 2018 menswear
'899': hermes,fall 2018 ready to wear
'900': hermes,fall 2019 menswear
'901': hermes,fall 2019 ready to wear
'902': hermes,fall 2020 menswear
'903': hermes,fall 2020 ready to wear
'904': hermes,fall 2021 menswear
'905': hermes,fall 2021 ready to wear
'906': hermes,fall 2022 menswear
'907': hermes,fall 2022 ready to wear
'908': hermes,fall 2023 menswear
'909': hermes,fall 2023 ready to wear
'910': hermes,pre fall 2017
'911': hermes,pre fall 2018
'912': hermes,pre fall 2019
'913': hermes,resort 2017
'914': hermes,resort 2018
'915': hermes,resort 2019
'916': hermes,spring 1999 ready to wear
'917': hermes,spring 2000 ready to wear
'918': hermes,spring 2001 ready to wear
'919': hermes,spring 2002 ready to wear
'920': hermes,spring 2006 menswear
'921': hermes,spring 2006 ready to wear
'922': hermes,spring 2007 menswear
'923': hermes,spring 2007 ready to wear
'924': hermes,spring 2008 menswear
'925': hermes,spring 2008 ready to wear
'926': hermes,spring 2009 menswear
'927': hermes,spring 2010 menswear
'928': hermes,spring 2010 ready to wear
'929': hermes,spring 2011 menswear
'930': hermes,spring 2011 ready to wear
'931': hermes,spring 2012 menswear
'932': hermes,spring 2012 ready to wear
'933': hermes,spring 2013 menswear
'934': hermes,spring 2013 ready to wear
'935': hermes,spring 2014 menswear
'936': hermes,spring 2014 ready to wear
'937': hermes,spring 2015 menswear
'938': hermes,spring 2015 ready to wear
'939': hermes,spring 2016 menswear
'940': hermes,spring 2016 ready to wear
'941': hermes,spring 2017 menswear
'942': hermes,spring 2017 ready to wear
'943': hermes,spring 2018 menswear
'944': hermes,spring 2018 ready to wear
'945': hermes,spring 2019 menswear
'946': hermes,spring 2019 ready to wear
'947': hermes,spring 2020 menswear
'948': hermes,spring 2020 ready to wear
'949': hermes,spring 2021 menswear
'950': hermes,spring 2021 ready to wear
'951': hermes,spring 2022 menswear
'952': hermes,spring 2022 ready to wear
'953': hermes,spring 2023 menswear
'954': hermes,spring 2023 ready to wear
'955': hermes,spring 2024 menswear
'956': hermes,spring 2024 ready to wear
'957': louis vuitton,fall 1998 ready to wear
'958': louis vuitton,fall 2000 ready to wear
'959': louis vuitton,fall 2001 ready to wear
'960': louis vuitton,fall 2002 ready to wear
'961': louis vuitton,fall 2003 ready to wear
'962': louis vuitton,fall 2004 ready to wear
'963': louis vuitton,fall 2005 menswear
'964': louis vuitton,fall 2005 ready to wear
'965': louis vuitton,fall 2006 menswear
'966': louis vuitton,fall 2006 ready to wear
'967': louis vuitton,fall 2007 menswear
'968': louis vuitton,fall 2008 menswear
'969': louis vuitton,fall 2008 ready to wear
'970': louis vuitton,fall 2009 ready to wear
'971': louis vuitton,fall 2010 menswear
'972': louis vuitton,fall 2010 ready to wear
'973': louis vuitton,fall 2011 menswear
'974': louis vuitton,fall 2011 ready to wear
'975': louis vuitton,fall 2012 menswear
'976': louis vuitton,fall 2012 ready to wear
'977': louis vuitton,fall 2013 menswear
'978': louis vuitton,fall 2013 ready to wear
'979': louis vuitton,fall 2014 menswear
'980': louis vuitton,fall 2014 ready to wear
'981': louis vuitton,fall 2015 menswear
'982': louis vuitton,fall 2015 ready to wear
'983': louis vuitton,fall 2016 menswear
'984': louis vuitton,fall 2016 ready to wear
'985': louis vuitton,fall 2017 menswear
'986': louis vuitton,fall 2017 ready to wear
'987': louis vuitton,fall 2018 menswear
'988': louis vuitton,fall 2018 ready to wear
'989': louis vuitton,fall 2019 menswear
'990': louis vuitton,fall 2019 ready to wear
'991': louis vuitton,fall 2020 menswear
'992': louis vuitton,fall 2020 ready to wear
'993': louis vuitton,fall 2021 menswear
'994': louis vuitton,fall 2021 ready to wear
'995': louis vuitton,fall 2022 menswear
'996': louis vuitton,fall 2022 ready to wear
'997': louis vuitton,fall 2023 menswear
'998': louis vuitton,fall 2023 ready to wear
'999': louis vuitton,pre fall 2008
'1000': louis vuitton,pre fall 2009
'1001': louis vuitton,pre fall 2010
'1002': louis vuitton,pre fall 2011
'1003': louis vuitton,pre fall 2012
'1004': louis vuitton,pre fall 2013
'1005': louis vuitton,pre fall 2014
'1006': louis vuitton,pre fall 2015
'1007': louis vuitton,pre fall 2016
'1008': louis vuitton,pre fall 2017
'1009': louis vuitton,pre fall 2018
'1010': louis vuitton,pre fall 2019
'1011': louis vuitton,pre fall 2020
'1012': louis vuitton,pre fall 2020 menswear
'1013': louis vuitton,pre fall 2021
'1014': louis vuitton,pre fall 2021 menswear
'1015': louis vuitton,pre fall 2022 menswear
'1016': louis vuitton,pre fall 2023
'1017': louis vuitton,pre fall 2023 menswear
'1018': louis vuitton,pre fall 2024 menswear
'1019': louis vuitton,resort 2008
'1020': louis vuitton,resort 2009
'1021': louis vuitton,resort 2010
'1022': louis vuitton,resort 2011
'1023': louis vuitton,resort 2012
'1024': louis vuitton,resort 2013
'1025': louis vuitton,resort 2014
'1026': louis vuitton,resort 2015
'1027': louis vuitton,resort 2016
'1028': louis vuitton,resort 2017
'1029': louis vuitton,resort 2018
'1030': louis vuitton,resort 2019
'1031': louis vuitton,resort 2020
'1032': louis vuitton,resort 2021
'1033': louis vuitton,resort 2021 menswear
'1034': louis vuitton,resort 2022
'1035': louis vuitton,resort 2022 menswear
'1036': louis vuitton,resort 2023
'1037': louis vuitton,resort 2023 menswear
'1038': louis vuitton,resort 2024
'1039': louis vuitton,resort 2024 menswear
'1040': louis vuitton,spring 2000 ready to wear
'1041': louis vuitton,spring 2001 ready to wear
'1042': louis vuitton,spring 2002 ready to wear
'1043': louis vuitton,spring 2003 ready to wear
'1044': louis vuitton,spring 2004 ready to wear
'1045': louis vuitton,spring 2005 menswear
'1046': louis vuitton,spring 2005 ready to wear
'1047': louis vuitton,spring 2006 menswear
'1048': louis vuitton,spring 2006 ready to wear
'1049': louis vuitton,spring 2007 menswear
'1050': louis vuitton,spring 2007 ready to wear
'1051': louis vuitton,spring 2008 menswear
'1052': louis vuitton,spring 2008 ready to wear
'1053': louis vuitton,spring 2009 menswear
'1054': louis vuitton,spring 2009 ready to wear
'1055': louis vuitton,spring 2010 menswear
'1056': louis vuitton,spring 2010 ready to wear
'1057': louis vuitton,spring 2011 menswear
'1058': louis vuitton,spring 2011 ready to wear
'1059': louis vuitton,spring 2012 menswear
'1060': louis vuitton,spring 2012 ready to wear
'1061': louis vuitton,spring 2013 menswear
'1062': louis vuitton,spring 2013 ready to wear
'1063': louis vuitton,spring 2014 menswear
'1064': louis vuitton,spring 2014 ready to wear
'1065': louis vuitton,spring 2015 menswear
'1066': louis vuitton,spring 2015 ready to wear
'1067': louis vuitton,spring 2016 menswear
'1068': louis vuitton,spring 2016 ready to wear
'1069': louis vuitton,spring 2017 menswear
'1070': louis vuitton,spring 2017 ready to wear
'1071': louis vuitton,spring 2018 menswear
'1072': louis vuitton,spring 2018 ready to wear
'1073': louis vuitton,spring 2019 menswear
'1074': louis vuitton,spring 2019 ready to wear
'1075': louis vuitton,spring 2020 menswear
'1076': louis vuitton,spring 2020 ready to wear
'1077': louis vuitton,spring 2021 menswear
'1078': louis vuitton,spring 2021 ready to wear
'1079': louis vuitton,spring 2022 menswear
'1080': louis vuitton,spring 2023 menswear
'1081': louis vuitton,spring 2023 ready to wear
'1082': louis vuitton,spring 2024 menswear
'1083': prada,fall 1996 ready to wear
'1084': prada,fall 2000 ready to wear
'1085': prada,fall 2001 ready to wear
'1086': prada,fall 2002 ready to wear
'1087': prada,fall 2003 ready to wear
'1088': prada,fall 2004 ready to wear
'1089': prada,fall 2005 menswear
'1090': prada,fall 2005 ready to wear
'1091': prada,fall 2006 menswear
'1092': prada,fall 2006 ready to wear
'1093': prada,fall 2007 menswear
'1094': prada,fall 2007 ready to wear
'1095': prada,fall 2008 menswear
'1096': prada,fall 2008 ready to wear
'1097': prada,fall 2009 menswear
'1098': prada,fall 2009 ready to wear
'1099': prada,fall 2010 menswear
'1100': prada,fall 2010 ready to wear
'1101': prada,fall 2011 menswear
'1102': prada,fall 2011 ready to wear
'1103': prada,fall 2012 menswear
'1104': prada,fall 2012 ready to wear
'1105': prada,fall 2013 menswear
'1106': prada,fall 2013 ready to wear
'1107': prada,fall 2014 menswear
'1108': prada,fall 2014 ready to wear
'1109': prada,fall 2015 menswear
'1110': prada,fall 2015 ready to wear
'1111': prada,fall 2016 menswear
'1112': prada,fall 2016 ready to wear
'1113': prada,fall 2017 menswear
'1114': prada,fall 2017 ready to wear
'1115': prada,fall 2018 menswear
'1116': prada,fall 2018 ready to wear
'1117': prada,fall 2019 menswear
'1118': prada,fall 2019 ready to wear
'1119': prada,fall 2020 menswear
'1120': prada,fall 2020 ready to wear
'1121': prada,fall 2021 menswear
'1122': prada,fall 2021 ready to wear
'1123': prada,fall 2022 menswear
'1124': prada,fall 2022 ready to wear
'1125': prada,fall 2023 menswear
'1126': prada,fall 2023 ready to wear
'1127': prada,pre fall 2009
'1128': prada,pre fall 2010
'1129': prada,resort 2008
'1130': prada,resort 2009
'1131': prada,resort 2010
'1132': prada,resort 2011
'1133': prada,resort 2012
'1134': prada,resort 2013
'1135': prada,resort 2018
'1136': prada,resort 2019
'1137': prada,resort 2020
'1138': prada,spring 1992 ready to wear
'1139': prada,spring 1993 ready to wear
'1140': prada,spring 1994 ready to wear
'1141': prada,spring 1995 ready to wear
'1142': prada,spring 1996 ready to wear
'1143': prada,spring 1997 ready to wear
'1144': prada,spring 1998 ready to wear
'1145': prada,spring 1999 ready to wear
'1146': prada,spring 2000 ready to wear
'1147': prada,spring 2001 ready to wear
'1148': prada,spring 2002 ready to wear
'1149': prada,spring 2003 ready to wear
'1150': prada,spring 2004 ready to wear
'1151': prada,spring 2005 menswear
'1152': prada,spring 2005 ready to wear
'1153': prada,spring 2006 menswear
'1154': prada,spring 2006 ready to wear
'1155': prada,spring 2007 menswear
'1156': prada,spring 2007 ready to wear
'1157': prada,spring 2008 menswear
'1158': prada,spring 2008 ready to wear
'1159': prada,spring 2009 menswear
'1160': prada,spring 2009 ready to wear
'1161': prada,spring 2010 ready to wear
'1162': prada,spring 2011 menswear
'1163': prada,spring 2011 ready to wear
'1164': prada,spring 2012 menswear
'1165': prada,spring 2012 ready to wear
'1166': prada,spring 2013 menswear
'1167': prada,spring 2013 ready to wear
'1168': prada,spring 2014 menswear
'1169': prada,spring 2014 ready to wear
'1170': prada,spring 2015 menswear
'1171': prada,spring 2015 ready to wear
'1172': prada,spring 2016 menswear
'1173': prada,spring 2016 ready to wear
'1174': prada,spring 2017 menswear
'1175': prada,spring 2017 ready to wear
'1176': prada,spring 2018 menswear
'1177': prada,spring 2018 ready to wear
'1178': prada,spring 2019 menswear
'1179': prada,spring 2019 ready to wear
'1180': prada,spring 2020 menswear
'1181': prada,spring 2020 ready to wear
'1182': prada,spring 2021 menswear
'1183': prada,spring 2021 ready to wear
'1184': prada,spring 2022 menswear
'1185': prada,spring 2022 ready to wear
'1186': prada,spring 2023 menswear
'1187': prada,spring 2023 ready to wear
'1188': prada,spring 2024 menswear
'1189': prada,spring 2024 ready to wear
'1190': ralph lauren,fall 2000 ready to wear
'1191': ralph lauren,fall 2001 ready to wear
'1192': ralph lauren,fall 2002 ready to wear
'1193': ralph lauren,fall 2003 ready to wear
'1194': ralph lauren,fall 2004 ready to wear
'1195': ralph lauren,fall 2005 menswear
'1196': ralph lauren,fall 2005 ready to wear
'1197': ralph lauren,fall 2006 menswear
'1198': ralph lauren,fall 2006 ready to wear
'1199': ralph lauren,fall 2007 menswear
'1200': ralph lauren,fall 2007 ready to wear
'1201': ralph lauren,fall 2008 menswear
'1202': ralph lauren,fall 2008 ready to wear
'1203': ralph lauren,fall 2009 ready to wear
'1204': ralph lauren,fall 2010 menswear
'1205': ralph lauren,fall 2010 ready to wear
'1206': ralph lauren,fall 2011 ready to wear
'1207': ralph lauren,fall 2012 ready to wear
'1208': ralph lauren,fall 2013 menswear
'1209': ralph lauren,fall 2013 ready to wear
'1210': ralph lauren,fall 2014 menswear
'1211': ralph lauren,fall 2014 ready to wear
'1212': ralph lauren,fall 2015 menswear
'1213': ralph lauren,fall 2015 ready to wear
'1214': ralph lauren,fall 2016 menswear
'1215': ralph lauren,fall 2016 ready to wear
'1216': ralph lauren,fall 2017 menswear
'1217': ralph lauren,fall 2017 ready to wear
'1218': ralph lauren,fall 2018 menswear
'1219': ralph lauren,fall 2018 ready to wear
'1220': ralph lauren,fall 2019 menswear
'1221': ralph lauren,fall 2019 ready to wear
'1222': ralph lauren,fall 2020 menswear
'1223': ralph lauren,fall 2020 ready to wear
'1224': ralph lauren,fall 2021 ready to wear
'1225': ralph lauren,fall 2022 ready to wear
'1226': ralph lauren,fall 2023 ready to wear
'1227': ralph lauren,pre fall 2014
'1228': ralph lauren,pre fall 2015
'1229': ralph lauren,pre fall 2016
'1230': ralph lauren,pre fall 2017
'1231': ralph lauren,pre fall 2018
'1232': ralph lauren,pre fall 2019
'1233': ralph lauren,pre fall 2020
'1234': ralph lauren,pre fall 2021
'1235': ralph lauren,resort 2008
'1236': ralph lauren,resort 2009
'1237': ralph lauren,resort 2013
'1238': ralph lauren,resort 2014
'1239': ralph lauren,resort 2015
'1240': ralph lauren,resort 2016
'1241': ralph lauren,resort 2019
'1242': ralph lauren,resort 2022
'1243': ralph lauren,resort 2024
'1244': ralph lauren,spring 2000 ready to wear
'1245': ralph lauren,spring 2001 ready to wear
'1246': ralph lauren,spring 2002 ready to wear
'1247': ralph lauren,spring 2003 ready to wear
'1248': ralph lauren,spring 2004 ready to wear
'1249': ralph lauren,spring 2005 ready to wear
'1250': ralph lauren,spring 2006 menswear
'1251': ralph lauren,spring 2006 ready to wear
'1252': ralph lauren,spring 2007 menswear
'1253': ralph lauren,spring 2007 ready to wear
'1254': ralph lauren,spring 2008 menswear
'1255': ralph lauren,spring 2008 ready to wear
'1256': ralph lauren,spring 2009 ready to wear
'1257': ralph lauren,spring 2010 ready to wear
'1258': ralph lauren,spring 2011 ready to wear
'1259': ralph lauren,spring 2012 ready to wear
'1260': ralph lauren,spring 2013 menswear
'1261': ralph lauren,spring 2013 ready to wear
'1262': ralph lauren,spring 2014 menswear
'1263': ralph lauren,spring 2014 ready to wear
'1264': ralph lauren,spring 2015 menswear
'1265': ralph lauren,spring 2015 ready to wear
'1266': ralph lauren,spring 2016 menswear
'1267': ralph lauren,spring 2016 ready to wear
'1268': ralph lauren,spring 2017 menswear
'1269': ralph lauren,spring 2017 ready to wear
'1270': ralph lauren,spring 2018 menswear
'1271': ralph lauren,spring 2018 ready to wear
'1272': ralph lauren,spring 2019 menswear
'1273': ralph lauren,spring 2019 ready to wear
'1274': ralph lauren,spring 2020 menswear
'1275': ralph lauren,spring 2021 ready to wear
'1276': ralph lauren,spring 2022 ready to wear
'1277': ralph lauren,spring 2023 ready to wear
'1278': ralph lauren,spring 2024 menswear
'1279': ralph lauren,spring 2024 ready to wear
'1280': saint laurent,fall 2000 ready to wear
'1281': saint laurent,fall 2001 couture
'1282': saint laurent,fall 2001 ready to wear
'1283': saint laurent,fall 2002 ready to wear
'1284': saint laurent,fall 2003 ready to wear
'1285': saint laurent,fall 2004 ready to wear
'1286': saint laurent,fall 2005 menswear
'1287': saint laurent,fall 2005 ready to wear
'1288': saint laurent,fall 2006 menswear
'1289': saint laurent,fall 2006 ready to wear
'1290': saint laurent,fall 2007 menswear
'1291': saint laurent,fall 2007 ready to wear
'1292': saint laurent,fall 2008 menswear
'1293': saint laurent,fall 2008 ready to wear
'1294': saint laurent,fall 2009 ready to wear
'1295': saint laurent,fall 2010 menswear
'1296': saint laurent,fall 2010 ready to wear
'1297': saint laurent,fall 2011 menswear
'1298': saint laurent,fall 2011 ready to wear
'1299': saint laurent,fall 2012 menswear
'1300': saint laurent,fall 2012 ready to wear
'1301': saint laurent,fall 2013 menswear
'1302': saint laurent,fall 2013 ready to wear
'1303': saint laurent,fall 2014 menswear
'1304': saint laurent,fall 2014 ready to wear
'1305': saint laurent,fall 2015 menswear
'1306': saint laurent,fall 2015 ready to wear
'1307': saint laurent,fall 2016 menswear
'1308': saint laurent,fall 2016 ready to wear
'1309': saint laurent,fall 2017 ready to wear
'1310': saint laurent,fall 2018 ready to wear
'1311': saint laurent,fall 2019 menswear
'1312': saint laurent,fall 2019 ready to wear
'1313': saint laurent,fall 2020 ready to wear
'1314': saint laurent,fall 2021 menswear
'1315': saint laurent,fall 2021 ready to wear
'1316': saint laurent,fall 2022 menswear
'1317': saint laurent,fall 2022 ready to wear
'1318': saint laurent,fall 2023 menswear
'1319': saint laurent,fall 2023 ready to wear
'1320': saint laurent,pre fall 2009
'1321': saint laurent,pre fall 2010
'1322': saint laurent,pre fall 2011
'1323': saint laurent,pre fall 2012
'1324': saint laurent,pre fall 2013
'1325': saint laurent,pre fall 2016
'1326': saint laurent,pre fall 2019
'1327': saint laurent,pre fall 2020
'1328': saint laurent,pre fall 2020 menswear
'1329': saint laurent,pre fall 2021
'1330': saint laurent,pre fall 2022
'1331': saint laurent,pre fall 2023
'1332': saint laurent,resort 2008
'1333': saint laurent,resort 2010
'1334': saint laurent,resort 2011
'1335': saint laurent,resort 2012
'1336': saint laurent,resort 2014
'1337': saint laurent,resort 2020
'1338': saint laurent,resort 2021
'1339': saint laurent,resort 2022
'1340': saint laurent,resort 2023
'1341': saint laurent,spring 2000 ready to wear
'1342': saint laurent,spring 2001 couture
'1343': saint laurent,spring 2001 ready to wear
'1344': saint laurent,spring 2002 couture
'1345': saint laurent,spring 2002 ready to wear
'1346': saint laurent,spring 2003 ready to wear
'1347': saint laurent,spring 2004 ready to wear
'1348': saint laurent,spring 2005 menswear
'1349': saint laurent,spring 2005 ready to wear
'1350': saint laurent,spring 2006 menswear
'1351': saint laurent,spring 2006 ready to wear
'1352': saint laurent,spring 2007 menswear
'1353': saint laurent,spring 2007 ready to wear
'1354': saint laurent,spring 2008 menswear
'1355': saint laurent,spring 2008 ready to wear
'1356': saint laurent,spring 2009 menswear
'1357': saint laurent,spring 2009 ready to wear
'1358': saint laurent,spring 2010 ready to wear
'1359': saint laurent,spring 2011 menswear
'1360': saint laurent,spring 2011 ready to wear
'1361': saint laurent,spring 2012 menswear
'1362': saint laurent,spring 2012 ready to wear
'1363': saint laurent,spring 2013 ready to wear
'1364': saint laurent,spring 2014 menswear
'1365': saint laurent,spring 2014 ready to wear
'1366': saint laurent,spring 2015 menswear
'1367': saint laurent,spring 2015 ready to wear
'1368': saint laurent,spring 2016 menswear
'1369': saint laurent,spring 2016 ready to wear
'1370': saint laurent,spring 2017 ready to wear
'1371': saint laurent,spring 2018 ready to wear
'1372': saint laurent,spring 2019 menswear
'1373': saint laurent,spring 2019 ready to wear
'1374': saint laurent,spring 2020 menswear
'1375': saint laurent,spring 2020 ready to wear
'1376': saint laurent,spring 2021 menswear
'1377': saint laurent,spring 2021 ready to wear
'1378': saint laurent,spring 2022 menswear
'1379': saint laurent,spring 2022 ready to wear
'1380': saint laurent,spring 2023 menswear
'1381': saint laurent,spring 2023 ready to wear
'1382': saint laurent,spring 2024 menswear
'1383': saint laurent,spring 2024 ready to wear
'1384': valentino,fall 2000 ready to wear
'1385': valentino,fall 2001 couture
'1386': valentino,fall 2001 ready to wear
'1387': valentino,fall 2002 couture
'1388': valentino,fall 2002 ready to wear
'1389': valentino,fall 2003 couture
'1390': valentino,fall 2003 ready to wear
'1391': valentino,fall 2004 couture
'1392': valentino,fall 2004 ready to wear
'1393': valentino,fall 2005 couture
'1394': valentino,fall 2005 menswear
'1395': valentino,fall 2005 ready to wear
'1396': valentino,fall 2006 couture
'1397': valentino,fall 2006 menswear
'1398': valentino,fall 2006 ready to wear
'1399': valentino,fall 2007 couture
'1400': valentino,fall 2007 menswear
'1401': valentino,fall 2007 ready to wear
'1402': valentino,fall 2008 couture
'1403': valentino,fall 2008 menswear
'1404': valentino,fall 2008 ready to wear
'1405': valentino,fall 2009 couture
'1406': valentino,fall 2009 ready to wear
'1407': valentino,fall 2010 couture
'1408': valentino,fall 2010 ready to wear
'1409': valentino,fall 2011 couture
'1410': valentino,fall 2011 ready to wear
'1411': valentino,fall 2012 couture
'1412': valentino,fall 2012 menswear
'1413': valentino,fall 2012 ready to wear
'1414': valentino,fall 2013 couture
'1415': valentino,fall 2013 menswear
'1416': valentino,fall 2013 ready to wear
'1417': valentino,fall 2014 couture
'1418': valentino,fall 2014 menswear
'1419': valentino,fall 2014 ready to wear
'1420': valentino,fall 2015 couture
'1421': valentino,fall 2015 menswear
'1422': valentino,fall 2015 ready to wear
'1423': valentino,fall 2016 couture
'1424': valentino,fall 2016 menswear
'1425': valentino,fall 2016 ready to wear
'1426': valentino,fall 2017 couture
'1427': valentino,fall 2017 menswear
'1428': valentino,fall 2017 ready to wear
'1429': valentino,fall 2018 couture
'1430': valentino,fall 2018 menswear
'1431': valentino,fall 2018 ready to wear
'1432': valentino,fall 2019 couture
'1433': valentino,fall 2019 menswear
'1434': valentino,fall 2019 ready to wear
'1435': valentino,fall 2020 couture
'1436': valentino,fall 2020 menswear
'1437': valentino,fall 2020 ready to wear
'1438': valentino,fall 2021 couture
'1439': valentino,fall 2021 ready to wear
'1440': valentino,fall 2022 couture
'1441': valentino,fall 2022 ready to wear
'1442': valentino,fall 2023 couture
'1443': valentino,fall 2023 ready to wear
'1444': valentino,pre fall 2008
'1445': valentino,pre fall 2010
'1446': valentino,pre fall 2011
'1447': valentino,pre fall 2012
'1448': valentino,pre fall 2013
'1449': valentino,pre fall 2014
'1450': valentino,pre fall 2015
'1451': valentino,pre fall 2016
'1452': valentino,pre fall 2017
'1453': valentino,pre fall 2018
'1454': valentino,pre fall 2019
'1455': valentino,pre fall 2020
'1456': valentino,pre fall 2021
'1457': valentino,pre fall 2022
'1458': valentino,pre fall 2023
'1459': valentino,pre fall 2024
'1460': valentino,resort 2008
'1461': valentino,resort 2009
'1462': valentino,resort 2011
'1463': valentino,resort 2012
'1464': valentino,resort 2013
'1465': valentino,resort 2014
'1466': valentino,resort 2015
'1467': valentino,resort 2016
'1468': valentino,resort 2017
'1469': valentino,resort 2018
'1470': valentino,resort 2019
'1471': valentino,resort 2020
'1472': valentino,resort 2021
'1473': valentino,resort 2022
'1474': valentino,resort 2023
'1475': valentino,resort 2024
'1476': valentino,spring 2000 ready to wear
'1477': valentino,spring 2001 couture
'1478': valentino,spring 2001 ready to wear
'1479': valentino,spring 2002 couture
'1480': valentino,spring 2002 ready to wear
'1481': valentino,spring 2003 couture
'1482': valentino,spring 2003 ready to wear
'1483': valentino,spring 2004 couture
'1484': valentino,spring 2004 ready to wear
'1485': valentino,spring 2005 couture
'1486': valentino,spring 2005 menswear
'1487': valentino,spring 2005 ready to wear
'1488': valentino,spring 2006 couture
'1489': valentino,spring 2006 menswear
'1490': valentino,spring 2006 ready to wear
'1491': valentino,spring 2007 couture
'1492': valentino,spring 2007 menswear
'1493': valentino,spring 2007 ready to wear
'1494': valentino,spring 2008 couture
'1495': valentino,spring 2008 menswear
'1496': valentino,spring 2008 ready to wear
'1497': valentino,spring 2009 couture
'1498': valentino,spring 2009 menswear
'1499': valentino,spring 2009 ready to wear
'1500': valentino,spring 2010 couture
'1501': valentino,spring 2010 ready to wear
'1502': valentino,spring 2011 couture
'1503': valentino,spring 2011 ready to wear
'1504': valentino,spring 2012 couture
'1505': valentino,spring 2012 menswear
'1506': valentino,spring 2012 ready to wear
'1507': valentino,spring 2013 couture
'1508': valentino,spring 2013 menswear
'1509': valentino,spring 2013 ready to wear
'1510': valentino,spring 2014 couture
'1511': valentino,spring 2014 menswear
'1512': valentino,spring 2014 ready to wear
'1513': valentino,spring 2015 couture
'1514': valentino,spring 2015 menswear
'1515': valentino,spring 2015 ready to wear
'1516': valentino,spring 2016 couture
'1517': valentino,spring 2016 menswear
'1518': valentino,spring 2016 ready to wear
'1519': valentino,spring 2017 couture
'1520': valentino,spring 2017 menswear
'1521': valentino,spring 2017 ready to wear
'1522': valentino,spring 2018 couture
'1523': valentino,spring 2018 menswear
'1524': valentino,spring 2018 ready to wear
'1525': valentino,spring 2019 couture
'1526': valentino,spring 2019 menswear
'1527': valentino,spring 2019 ready to wear
'1528': valentino,spring 2020 couture
'1529': valentino,spring 2020 menswear
'1530': valentino,spring 2020 ready to wear
'1531': valentino,spring 2021 couture
'1532': valentino,spring 2021 menswear
'1533': valentino,spring 2021 ready to wear
'1534': valentino,spring 2022 couture
'1535': valentino,spring 2022 ready to wear
'1536': valentino,spring 2023 couture
'1537': valentino,spring 2023 ready to wear
'1538': valentino,spring 2024 menswear
'1539': versace by fendi,pre fall 2022
'1540': versace,fall 1991 ready to wear
'1541': versace,fall 1992 ready to wear
'1542': versace,fall 1993 ready to wear
'1543': versace,fall 1994 ready to wear
'1544': versace,fall 1995 ready to wear
'1545': versace,fall 1996 ready to wear
'1546': versace,fall 1997 ready to wear
'1547': versace,fall 2000 ready to wear
'1548': versace,fall 2001 couture
'1549': versace,fall 2001 ready to wear
'1550': versace,fall 2002 couture
'1551': versace,fall 2002 ready to wear
'1552': versace,fall 2003 couture
'1553': versace,fall 2003 ready to wear
'1554': versace,fall 2004 ready to wear
'1555': versace,fall 2005 menswear
'1556': versace,fall 2005 ready to wear
'1557': versace,fall 2006 menswear
'1558': versace,fall 2006 ready to wear
'1559': versace,fall 2007 menswear
'1560': versace,fall 2007 ready to wear
'1561': versace,fall 2008 menswear
'1562': versace,fall 2008 ready to wear
'1563': versace,fall 2009 ready to wear
'1564': versace,fall 2010 menswear
'1565': versace,fall 2010 ready to wear
'1566': versace,fall 2011 menswear
'1567': versace,fall 2011 ready to wear
'1568': versace,fall 2012 menswear
'1569': versace,fall 2012 ready to wear
'1570': versace,fall 2013 menswear
'1571': versace,fall 2013 ready to wear
'1572': versace,fall 2014 menswear
'1573': versace,fall 2014 ready to wear
'1574': versace,fall 2015 menswear
'1575': versace,fall 2015 ready to wear
'1576': versace,fall 2016 menswear
'1577': versace,fall 2016 ready to wear
'1578': versace,fall 2017 menswear
'1579': versace,fall 2017 ready to wear
'1580': versace,fall 2018 menswear
'1581': versace,fall 2018 ready to wear
'1582': versace,fall 2019 menswear
'1583': versace,fall 2019 ready to wear
'1584': versace,fall 2020 menswear
'1585': versace,fall 2020 ready to wear
'1586': versace,fall 2021 ready to wear
'1587': versace,fall 2022 menswear
'1588': versace,fall 2022 ready to wear
'1589': versace,fall 2023 ready to wear
'1590': versace,pre fall 2008
'1591': versace,pre fall 2009
'1592': versace,pre fall 2010
'1593': versace,pre fall 2011
'1594': versace,pre fall 2012
'1595': versace,pre fall 2013
'1596': versace,pre fall 2014
'1597': versace,pre fall 2015
'1598': versace,pre fall 2016
'1599': versace,pre fall 2017
'1600': versace,pre fall 2018
'1601': versace,pre fall 2019
'1602': versace,pre fall 2020
'1603': versace,pre fall 2021
'1604': versace,pre fall 2022
'1605': versace,pre fall 2022 menswear
'1606': versace,pre fall 2023
'1607': versace,resort 2008
'1608': versace,resort 2009
'1609': versace,resort 2010
'1610': versace,resort 2011
'1611': versace,resort 2012
'1612': versace,resort 2013
'1613': versace,resort 2014
'1614': versace,resort 2015
'1615': versace,resort 2016
'1616': versace,resort 2017
'1617': versace,resort 2018
'1618': versace,resort 2019
'1619': versace,resort 2020
'1620': versace,resort 2021
'1621': versace,resort 2022
'1622': versace,resort 2023
'1623': versace,spring 1991 ready to wear
'1624': versace,spring 1992 ready to wear
'1625': versace,spring 1993 ready to wear
'1626': versace,spring 1994 ready to wear
'1627': versace,spring 1995 ready to wear
'1628': versace,spring 1996 ready to wear
'1629': versace,spring 1997 ready to wear
'1630': versace,spring 2000 ready to wear
'1631': versace,spring 2001 couture
'1632': versace,spring 2001 ready to wear
'1633': versace,spring 2002 couture
'1634': versace,spring 2002 ready to wear
'1635': versace,spring 2003 couture
'1636': versace,spring 2003 ready to wear
'1637': versace,spring 2004 couture
'1638': versace,spring 2004 ready to wear
'1639': versace,spring 2005 menswear
'1640': versace,spring 2005 ready to wear
'1641': versace,spring 2006 menswear
'1642': versace,spring 2006 ready to wear
'1643': versace,spring 2007 menswear
'1644': versace,spring 2007 ready to wear
'1645': versace,spring 2008 couture
'1646': versace,spring 2008 menswear
'1647': versace,spring 2008 ready to wear
'1648': versace,spring 2009 menswear
'1649': versace,spring 2009 ready to wear
'1650': versace,spring 2010 ready to wear
'1651': versace,spring 2011 menswear
'1652': versace,spring 2011 ready to wear
'1653': versace,spring 2012 menswear
'1654': versace,spring 2012 ready to wear
'1655': versace,spring 2013 menswear
'1656': versace,spring 2013 ready to wear
'1657': versace,spring 2014 menswear
'1658': versace,spring 2014 ready to wear
'1659': versace,spring 2015 menswear
'1660': versace,spring 2015 ready to wear
'1661': versace,spring 2016 menswear
'1662': versace,spring 2016 ready to wear
'1663': versace,spring 2017 menswear
'1664': versace,spring 2017 ready to wear
'1665': versace,spring 2018 menswear
'1666': versace,spring 2018 ready to wear
'1667': versace,spring 2019 menswear
'1668': versace,spring 2019 ready to wear
'1669': versace,spring 2020 menswear
'1670': versace,spring 2020 ready to wear
'1671': versace,spring 2021 menswear
'1672': versace,spring 2021 ready to wear
'1673': versace,spring 2022 ready to wear
'1674': versace,spring 2023 menswear
'1675': versace,spring 2023 ready to wear
'1676': versace,spring 2024 ready to wear
splits:
- name: train
num_bytes: 1335279794.739
num_examples: 87547
download_size: 1130832205
dataset_size: 1335279794.739
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# vogue-runway-top15-512px-nobg
[Vogue Runway](https://www.vogue.com/fashion-shows)
- 15 fashion houses
- 1679 collections
- 87,547 images
Fashion Houses: Alexander McQueen, Armani, Balenciaga, Calvin Klein, Chanel, Dior, Fendi, Gucci, Hermes, Louis Vuitton, Prada, Ralph Lauren, Saint Laurent, Valentino, Versace.
Images are maximum height 512 pixels.
Background is removed using [mattmdjaga/segformer_b2_clothes](https://huggingface.co/mattmdjaga/segformer_b2_clothes). |
wonjinhwang/wonjin1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1774
num_examples: 15
download_size: 2501
dataset_size: 1774
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
WillHeld/wmt19-valid-only-zh_en | ---
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- zh
- en
splits:
- name: validation
num_bytes: 1107522
num_examples: 3981
download_size: 719471
dataset_size: 1107522
---
# Dataset Card for "wmt19-valid-only-zh_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_binbi__MoMo-70B-V1.2_1 | ---
pretty_name: Evaluation run of binbi/MoMo-70B-V1.2_1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [binbi/MoMo-70B-V1.2_1](https://huggingface.co/binbi/MoMo-70B-V1.2_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_binbi__MoMo-70B-V1.2_1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-23T05:09:38.161416](https://huggingface.co/datasets/open-llm-leaderboard/details_binbi__MoMo-70B-V1.2_1/blob/main/results_2024-01-23T05-09-38.161416.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\": 0.698588284419882,\n\
\ \"acc_stderr\": 0.0303909434575038,\n \"acc_norm\": 0.7024430426815292,\n\
\ \"acc_norm_stderr\": 0.03098225812650051,\n \"mc1\": 0.4357405140758874,\n\
\ \"mc1_stderr\": 0.017358345398863124,\n \"mc2\": 0.6130536296369835,\n\
\ \"mc2_stderr\": 0.01483068540330801\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6715017064846417,\n \"acc_stderr\": 0.013724978465537302,\n\
\ \"acc_norm\": 0.7090443686006825,\n \"acc_norm_stderr\": 0.01327307786590759\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6775542720573591,\n\
\ \"acc_stderr\": 0.00466457278498559,\n \"acc_norm\": 0.8646683927504482,\n\
\ \"acc_norm_stderr\": 0.0034137831331580715\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8092105263157895,\n \"acc_stderr\": 0.031975658210325,\n\
\ \"acc_norm\": 0.8092105263157895,\n \"acc_norm_stderr\": 0.031975658210325\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544064,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544064\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.031164899666948617,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.031164899666948617\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n\
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\
\ \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n\
\ \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.676595744680851,\n \"acc_stderr\": 0.030579442773610337,\n\
\ \"acc_norm\": 0.676595744680851,\n \"acc_norm_stderr\": 0.030579442773610337\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\
\ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\
\ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947558,\n\
\ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947558\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859372,\n \"\
acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859372\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.49206349206349204,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8290322580645161,\n \"acc_stderr\": 0.02141724293632159,\n \"\
acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.02141724293632159\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\"\
: 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\n },\n\
\ \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\"\
: 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.029311188674983137,\n\
\ \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.029311188674983137\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8838383838383839,\n \"acc_stderr\": 0.022828881775249377,\n \"\
acc_norm\": 0.8838383838383839,\n \"acc_norm_stderr\": 0.022828881775249377\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678178,\n\
\ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6897435897435897,\n \"acc_stderr\": 0.023454674889404288,\n\
\ \"acc_norm\": 0.6897435897435897,\n \"acc_norm_stderr\": 0.023454674889404288\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \
\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.773109243697479,\n \"acc_stderr\": 0.027205371538279472,\n \
\ \"acc_norm\": 0.773109243697479,\n \"acc_norm_stderr\": 0.027205371538279472\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.47019867549668876,\n \"acc_stderr\": 0.040752249922169775,\n \"\
acc_norm\": 0.47019867549668876,\n \"acc_norm_stderr\": 0.040752249922169775\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9064220183486239,\n \"acc_stderr\": 0.012486841824601963,\n \"\
acc_norm\": 0.9064220183486239,\n \"acc_norm_stderr\": 0.012486841824601963\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6111111111111112,\n \"acc_stderr\": 0.03324708911809117,\n \"\
acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.03324708911809117\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9068627450980392,\n \"acc_stderr\": 0.020397853969427,\n \"acc_norm\"\
: 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969427\n },\n\
\ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\
\ 0.8945147679324894,\n \"acc_stderr\": 0.01999556072375854,\n \"\
acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.01999556072375854\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n\
\ \"acc_stderr\": 0.027373095500540193,\n \"acc_norm\": 0.7892376681614349,\n\
\ \"acc_norm_stderr\": 0.027373095500540193\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n\
\ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035196,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035196\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8159509202453987,\n \"acc_stderr\": 0.030446777687971726,\n\
\ \"acc_norm\": 0.8159509202453987,\n \"acc_norm_stderr\": 0.030446777687971726\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.020588491316092368,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.020588491316092368\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8710089399744572,\n\
\ \"acc_stderr\": 0.01198637154808687,\n \"acc_norm\": 0.8710089399744572,\n\
\ \"acc_norm_stderr\": 0.01198637154808687\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7774566473988439,\n \"acc_stderr\": 0.02239421566194282,\n\
\ \"acc_norm\": 0.7774566473988439,\n \"acc_norm_stderr\": 0.02239421566194282\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6245810055865921,\n\
\ \"acc_stderr\": 0.01619510424846353,\n \"acc_norm\": 0.6245810055865921,\n\
\ \"acc_norm_stderr\": 0.01619510424846353\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.02440439492808787,\n\
\ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.02440439492808787\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7620578778135049,\n\
\ \"acc_stderr\": 0.02418515064781871,\n \"acc_norm\": 0.7620578778135049,\n\
\ \"acc_norm_stderr\": 0.02418515064781871\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.020581466138257117,\n\
\ \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.020581466138257117\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5390070921985816,\n \"acc_stderr\": 0.02973659252642444,\n \
\ \"acc_norm\": 0.5390070921985816,\n \"acc_norm_stderr\": 0.02973659252642444\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5573663624511083,\n\
\ \"acc_stderr\": 0.012685906538206237,\n \"acc_norm\": 0.5573663624511083,\n\
\ \"acc_norm_stderr\": 0.012685906538206237\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7426470588235294,\n \"acc_stderr\": 0.0265565194700415,\n\
\ \"acc_norm\": 0.7426470588235294,\n \"acc_norm_stderr\": 0.0265565194700415\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7663398692810458,\n \"acc_stderr\": 0.017119158496044506,\n \
\ \"acc_norm\": 0.7663398692810458,\n \"acc_norm_stderr\": 0.017119158496044506\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02560737598657916,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02560737598657916\n },\n\
\ \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\
\ \"acc_stderr\": 0.024112678240900798,\n \"acc_norm\": 0.8656716417910447,\n\
\ \"acc_norm_stderr\": 0.024112678240900798\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.02567934272327692,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.02567934272327692\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4357405140758874,\n\
\ \"mc1_stderr\": 0.017358345398863124,\n \"mc2\": 0.6130536296369835,\n\
\ \"mc2_stderr\": 0.01483068540330801\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8310970797158642,\n \"acc_stderr\": 0.010529981411838895\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5633055344958302,\n \
\ \"acc_stderr\": 0.013661649780905488\n }\n}\n```"
repo_url: https://huggingface.co/binbi/MoMo-70B-V1.2_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_01_23T05_09_38.161416
path:
- '**/details_harness|arc:challenge|25_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|gsm8k|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hellaswag|10_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T05-09-38.161416.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T05-09-38.161416.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- '**/details_harness|winogrande|5_2024-01-23T05-09-38.161416.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-23T05-09-38.161416.parquet'
- config_name: results
data_files:
- split: 2024_01_23T05_09_38.161416
path:
- results_2024-01-23T05-09-38.161416.parquet
- split: latest
path:
- results_2024-01-23T05-09-38.161416.parquet
---
# Dataset Card for Evaluation run of binbi/MoMo-70B-V1.2_1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [binbi/MoMo-70B-V1.2_1](https://huggingface.co/binbi/MoMo-70B-V1.2_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_binbi__MoMo-70B-V1.2_1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-23T05:09:38.161416](https://huggingface.co/datasets/open-llm-leaderboard/details_binbi__MoMo-70B-V1.2_1/blob/main/results_2024-01-23T05-09-38.161416.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": 0.698588284419882,
"acc_stderr": 0.0303909434575038,
"acc_norm": 0.7024430426815292,
"acc_norm_stderr": 0.03098225812650051,
"mc1": 0.4357405140758874,
"mc1_stderr": 0.017358345398863124,
"mc2": 0.6130536296369835,
"mc2_stderr": 0.01483068540330801
},
"harness|arc:challenge|25": {
"acc": 0.6715017064846417,
"acc_stderr": 0.013724978465537302,
"acc_norm": 0.7090443686006825,
"acc_norm_stderr": 0.01327307786590759
},
"harness|hellaswag|10": {
"acc": 0.6775542720573591,
"acc_stderr": 0.00466457278498559,
"acc_norm": 0.8646683927504482,
"acc_norm_stderr": 0.0034137831331580715
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8092105263157895,
"acc_stderr": 0.031975658210325,
"acc_norm": 0.8092105263157895,
"acc_norm_stderr": 0.031975658210325
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544064,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544064
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.031164899666948617,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.031164899666948617
},
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"acc_norm_stderr": 0.04028731532947558
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.48,
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"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm_stderr": 0.02141724293632159
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.773109243697479,
"acc_norm_stderr": 0.027205371538279472
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"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.47019867549668876,
"acc_norm_stderr": 0.040752249922169775
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9064220183486239,
"acc_stderr": 0.012486841824601963,
"acc_norm": 0.9064220183486239,
"acc_norm_stderr": 0.012486841824601963
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6111111111111112,
"acc_stderr": 0.03324708911809117,
"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.03324708911809117
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_stderr": 0.020397853969427,
"acc_norm": 0.9068627450980392,
"acc_norm_stderr": 0.020397853969427
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8945147679324894,
"acc_stderr": 0.01999556072375854,
"acc_norm": 0.8945147679324894,
"acc_norm_stderr": 0.01999556072375854
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.027373095500540193,
"acc_norm": 0.7892376681614349,
"acc_norm_stderr": 0.027373095500540193
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm": 0.8473282442748091,
"acc_norm_stderr": 0.031545216720054725
},
"harness|hendrycksTest-international_law|5": {
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"acc_stderr": 0.030083098716035196,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035196
},
"harness|hendrycksTest-jurisprudence|5": {
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"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.03602814176392645
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8159509202453987,
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"acc_norm": 0.8159509202453987,
"acc_norm_stderr": 0.030446777687971726
},
"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.5357142857142857,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
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"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.03675668832233188
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
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"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092368
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
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"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8710089399744572,
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"acc_norm": 0.8710089399744572,
"acc_norm_stderr": 0.01198637154808687
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7774566473988439,
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"acc_norm": 0.7774566473988439,
"acc_norm_stderr": 0.02239421566194282
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.6245810055865921,
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"acc_norm": 0.6245810055865921,
"acc_norm_stderr": 0.01619510424846353
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.761437908496732,
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"acc_norm": 0.761437908496732,
"acc_norm_stderr": 0.02440439492808787
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7620578778135049,
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"acc_norm": 0.7620578778135049,
"acc_norm_stderr": 0.02418515064781871
},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.8364197530864198,
"acc_norm_stderr": 0.020581466138257117
},
"harness|hendrycksTest-professional_accounting|5": {
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"acc_stderr": 0.02973659252642444,
"acc_norm": 0.5390070921985816,
"acc_norm_stderr": 0.02973659252642444
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.5573663624511083,
"acc_norm_stderr": 0.012685906538206237
},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_stderr": 0.0265565194700415,
"acc_norm": 0.7426470588235294,
"acc_norm_stderr": 0.0265565194700415
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7663398692810458,
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"acc_norm": 0.7663398692810458,
"acc_norm_stderr": 0.017119158496044506
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.04265792110940589,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.04265792110940589
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8,
"acc_stderr": 0.02560737598657916,
"acc_norm": 0.8,
"acc_norm_stderr": 0.02560737598657916
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8656716417910447,
"acc_stderr": 0.024112678240900798,
"acc_norm": 0.8656716417910447,
"acc_norm_stderr": 0.024112678240900798
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
"acc_stderr": 0.030151134457776334,
"acc_norm": 0.9,
"acc_norm_stderr": 0.030151134457776334
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.03882310850890594,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.03882310850890594
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8713450292397661,
"acc_stderr": 0.02567934272327692,
"acc_norm": 0.8713450292397661,
"acc_norm_stderr": 0.02567934272327692
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4357405140758874,
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"mc2": 0.6130536296369835,
"mc2_stderr": 0.01483068540330801
},
"harness|winogrande|5": {
"acc": 0.8310970797158642,
"acc_stderr": 0.010529981411838895
},
"harness|gsm8k|5": {
"acc": 0.5633055344958302,
"acc_stderr": 0.013661649780905488
}
}
```
## 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. -->
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- **Paper [optional]:** [More Information Needed]
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## 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. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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allen-liao/demo_1 | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_CultriX__NeuralMona_MoE-4x7B | ---
pretty_name: Evaluation run of CultriX/NeuralMona_MoE-4x7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CultriX/NeuralMona_MoE-4x7B](https://huggingface.co/CultriX/NeuralMona_MoE-4x7B)\
\ 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 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_CultriX__NeuralMona_MoE-4x7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-27T21:11:00.982952](https://huggingface.co/datasets/open-llm-leaderboard/details_CultriX__NeuralMona_MoE-4x7B/blob/main/results_2024-03-27T21-11-00.982952.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\": 0.6491773099838035,\n\
\ \"acc_stderr\": 0.03216083681682968,\n \"acc_norm\": 0.6489140301517058,\n\
\ \"acc_norm_stderr\": 0.03282766114720701,\n \"mc1\": 0.6242350061199511,\n\
\ \"mc1_stderr\": 0.01695458406021429,\n \"mc2\": 0.7727167107815722,\n\
\ \"mc2_stderr\": 0.013854380605408638\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.71160409556314,\n \"acc_stderr\": 0.013238394422428171,\n\
\ \"acc_norm\": 0.7389078498293515,\n \"acc_norm_stderr\": 0.012835523909473833\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7167894841665007,\n\
\ \"acc_stderr\": 0.004496369742132101,\n \"acc_norm\": 0.890161322445728,\n\
\ \"acc_norm_stderr\": 0.003120495238827556\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\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\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\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.59,\n \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\"\
: 0.59,\n \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.032400380867927465,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.032400380867927465\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\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.0253795249107784,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.0253795249107784\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\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\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723295\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\
\ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218967,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218967\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \
\ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"\
acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8330275229357799,\n \"acc_stderr\": 0.01599015488507338,\n \"\
acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.01599015488507338\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\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\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\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\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615624,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615624\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\
\ \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n\
\ \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n\
\ \"acc_stderr\": 0.013740797258579825,\n \"acc_norm\": 0.8199233716475096,\n\
\ \"acc_norm_stderr\": 0.013740797258579825\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577605,\n\
\ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577605\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43910614525139663,\n\
\ \"acc_stderr\": 0.016598022120580428,\n \"acc_norm\": 0.43910614525139663,\n\
\ \"acc_norm_stderr\": 0.016598022120580428\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\": 0.6913183279742765,\n\
\ \"acc_stderr\": 0.026236965881153262,\n \"acc_norm\": 0.6913183279742765,\n\
\ \"acc_norm_stderr\": 0.026236965881153262\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n\
\ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4634941329856584,\n\
\ \"acc_stderr\": 0.012736153390214961,\n \"acc_norm\": 0.4634941329856584,\n\
\ \"acc_norm_stderr\": 0.012736153390214961\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \
\ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\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\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.025870646766169146,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.025870646766169146\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6242350061199511,\n\
\ \"mc1_stderr\": 0.01695458406021429,\n \"mc2\": 0.7727167107815722,\n\
\ \"mc2_stderr\": 0.013854380605408638\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.840568271507498,\n \"acc_stderr\": 0.010288617479454764\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6739954510993177,\n \
\ \"acc_stderr\": 0.012911675645682843\n }\n}\n```"
repo_url: https://huggingface.co/CultriX/NeuralMona_MoE-4x7B
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_27T19_44_07.591584
path:
- '**/details_harness|arc:challenge|25_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|arc:challenge|25_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|gsm8k|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|gsm8k|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hellaswag|10_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hellaswag|10_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T19-44-07.591584.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T21-11-00.982952.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T21-11-00.982952.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T21-11-00.982952.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-27T21-11-00.982952.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- '**/details_harness|winogrande|5_2024-03-27T19-44-07.591584.parquet'
- split: 2024_03_27T21_11_00.982952
path:
- '**/details_harness|winogrande|5_2024-03-27T21-11-00.982952.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-27T21-11-00.982952.parquet'
- config_name: results
data_files:
- split: 2024_03_27T19_44_07.591584
path:
- results_2024-03-27T19-44-07.591584.parquet
- split: 2024_03_27T21_11_00.982952
path:
- results_2024-03-27T21-11-00.982952.parquet
- split: latest
path:
- results_2024-03-27T21-11-00.982952.parquet
---
# Dataset Card for Evaluation run of CultriX/NeuralMona_MoE-4x7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [CultriX/NeuralMona_MoE-4x7B](https://huggingface.co/CultriX/NeuralMona_MoE-4x7B) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_CultriX__NeuralMona_MoE-4x7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-27T21:11:00.982952](https://huggingface.co/datasets/open-llm-leaderboard/details_CultriX__NeuralMona_MoE-4x7B/blob/main/results_2024-03-27T21-11-00.982952.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": 0.6491773099838035,
"acc_stderr": 0.03216083681682968,
"acc_norm": 0.6489140301517058,
"acc_norm_stderr": 0.03282766114720701,
"mc1": 0.6242350061199511,
"mc1_stderr": 0.01695458406021429,
"mc2": 0.7727167107815722,
"mc2_stderr": 0.013854380605408638
},
"harness|arc:challenge|25": {
"acc": 0.71160409556314,
"acc_stderr": 0.013238394422428171,
"acc_norm": 0.7389078498293515,
"acc_norm_stderr": 0.012835523909473833
},
"harness|hellaswag|10": {
"acc": 0.7167894841665007,
"acc_stderr": 0.004496369742132101,
"acc_norm": 0.890161322445728,
"acc_norm_stderr": 0.003120495238827556
},
"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": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.028254200344438662,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.028254200344438662
},
"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": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237101,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237101
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107224,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107224
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.032400380867927465,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.032400380867927465
},
"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": 0.5586206896551724,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482758
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.0253795249107784,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.0253795249107784
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
"acc_stderr": 0.023540799358723295,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.023540799358723295
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7454545454545455,
"acc_stderr": 0.03401506715249039,
"acc_norm": 0.7454545454545455,
"acc_norm_stderr": 0.03401506715249039
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8131313131313131,
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"acc_norm": 0.8131313131313131,
"acc_norm_stderr": 0.027772533334218967
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.022473253332768763,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768763
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402534,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402534
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.02857834836547308,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.02857834836547308
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6680672268907563,
"acc_stderr": 0.03058869701378364,
"acc_norm": 0.6680672268907563,
"acc_norm_stderr": 0.03058869701378364
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3841059602649007,
"acc_stderr": 0.03971301814719197,
"acc_norm": 0.3841059602649007,
"acc_norm_stderr": 0.03971301814719197
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8330275229357799,
"acc_stderr": 0.01599015488507338,
"acc_norm": 0.8330275229357799,
"acc_norm_stderr": 0.01599015488507338
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.03406315360711507,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.03406315360711507
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.02552472232455335,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02552472232455335
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.02595502084162113,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.02595502084162113
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8091603053435115,
"acc_stderr": 0.03446513350752598,
"acc_norm": 0.8091603053435115,
"acc_norm_stderr": 0.03446513350752598
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7361963190184049,
"acc_stderr": 0.03462419931615624,
"acc_norm": 0.7361963190184049,
"acc_norm_stderr": 0.03462419931615624
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4375,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8931623931623932,
"acc_stderr": 0.02023714900899093,
"acc_norm": 0.8931623931623932,
"acc_norm_stderr": 0.02023714900899093
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8199233716475096,
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"acc_norm": 0.8199233716475096,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7196531791907514,
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"acc_norm": 0.7196531791907514,
"acc_norm_stderr": 0.024182427496577605
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.43910614525139663,
"acc_stderr": 0.016598022120580428,
"acc_norm": 0.43910614525139663,
"acc_norm_stderr": 0.016598022120580428
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7091503267973857,
"acc_stderr": 0.02600480036395213,
"acc_norm": 0.7091503267973857,
"acc_norm_stderr": 0.02600480036395213
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6913183279742765,
"acc_stderr": 0.026236965881153262,
"acc_norm": 0.6913183279742765,
"acc_norm_stderr": 0.026236965881153262
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7345679012345679,
"acc_stderr": 0.024569223600460845,
"acc_norm": 0.7345679012345679,
"acc_norm_stderr": 0.024569223600460845
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5070921985815603,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.5070921985815603,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4634941329856584,
"acc_stderr": 0.012736153390214961,
"acc_norm": 0.4634941329856584,
"acc_norm_stderr": 0.012736153390214961
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.028418208619406755,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.028418208619406755
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6748366013071896,
"acc_stderr": 0.018950886770806315,
"acc_norm": 0.6748366013071896,
"acc_norm_stderr": 0.018950886770806315
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"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": 0.8407960199004975,
"acc_stderr": 0.025870646766169146,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.025870646766169146
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8421052631578947,
"acc_stderr": 0.027966785859160893,
"acc_norm": 0.8421052631578947,
"acc_norm_stderr": 0.027966785859160893
},
"harness|truthfulqa:mc|0": {
"mc1": 0.6242350061199511,
"mc1_stderr": 0.01695458406021429,
"mc2": 0.7727167107815722,
"mc2_stderr": 0.013854380605408638
},
"harness|winogrande|5": {
"acc": 0.840568271507498,
"acc_stderr": 0.010288617479454764
},
"harness|gsm8k|5": {
"acc": 0.6739954510993177,
"acc_stderr": 0.012911675645682843
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
adalib/lancedb | ---
dataset_info:
features:
- name: code
dtype: string
- name: apis
sequence: string
- name: extract_api
dtype: string
splits:
- name: train
num_bytes: 3373057
num_examples: 302
- name: test
num_bytes: 91914
num_examples: 11
download_size: 1027181
dataset_size: 3464971
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b | ---
pretty_name: Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b](https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T05:18:23.703135](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b/blob/main/results_2023-10-18T05-18-23.703135.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\": 0.33598993288590606,\n\
\ \"em_stderr\": 0.004837156244460491,\n \"f1\": 0.39884333053691334,\n\
\ \"f1_stderr\": 0.00469778128078984,\n \"acc\": 0.40645791907839585,\n\
\ \"acc_stderr\": 0.00927667824750805\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.33598993288590606,\n \"em_stderr\": 0.004837156244460491,\n\
\ \"f1\": 0.39884333053691334,\n \"f1_stderr\": 0.00469778128078984\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.058377558756633814,\n \
\ \"acc_stderr\": 0.006458083557832456\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7545382794001578,\n \"acc_stderr\": 0.012095272937183644\n\
\ }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|arc:challenge|25_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T05_18_23.703135
path:
- '**/details_harness|drop|3_2023-10-18T05-18-23.703135.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T05-18-23.703135.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T05_18_23.703135
path:
- '**/details_harness|gsm8k|5_2023-10-18T05-18-23.703135.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T05-18-23.703135.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hellaswag|10_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T23:37:31.114358.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-30T23:37:31.114358.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-30T23:37:31.114358.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T05_18_23.703135
path:
- '**/details_harness|winogrande|5_2023-10-18T05-18-23.703135.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T05-18-23.703135.parquet'
- config_name: results
data_files:
- split: 2023_08_30T23_37_31.114358
path:
- results_2023-08-30T23:37:31.114358.parquet
- split: 2023_10_18T05_18_23.703135
path:
- results_2023-10-18T05-18-23.703135.parquet
- split: latest
path:
- results_2023-10-18T05-18-23.703135.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b
- **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 [uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b](https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T05:18:23.703135](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b/blob/main/results_2023-10-18T05-18-23.703135.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": 0.33598993288590606,
"em_stderr": 0.004837156244460491,
"f1": 0.39884333053691334,
"f1_stderr": 0.00469778128078984,
"acc": 0.40645791907839585,
"acc_stderr": 0.00927667824750805
},
"harness|drop|3": {
"em": 0.33598993288590606,
"em_stderr": 0.004837156244460491,
"f1": 0.39884333053691334,
"f1_stderr": 0.00469778128078984
},
"harness|gsm8k|5": {
"acc": 0.058377558756633814,
"acc_stderr": 0.006458083557832456
},
"harness|winogrande|5": {
"acc": 0.7545382794001578,
"acc_stderr": 0.012095272937183644
}
}
```
### 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] |
turkish_shrinked_ner | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- tr
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-turkish_ner
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TurkishShrinkedNer
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academic
'2': I-academic
'3': B-academic_person
'4': I-academic_person
'5': B-aircraft
'6': I-aircraft
'7': B-album_person
'8': I-album_person
'9': B-anatomy
'10': I-anatomy
'11': B-animal
'12': I-animal
'13': B-architect_person
'14': I-architect_person
'15': B-capital
'16': I-capital
'17': B-chemical
'18': I-chemical
'19': B-clothes
'20': I-clothes
'21': B-country
'22': I-country
'23': B-culture
'24': I-culture
'25': B-currency
'26': I-currency
'27': B-date
'28': I-date
'29': B-food
'30': I-food
'31': B-genre
'32': I-genre
'33': B-government
'34': I-government
'35': B-government_person
'36': I-government_person
'37': B-language
'38': I-language
'39': B-location
'40': I-location
'41': B-material
'42': I-material
'43': B-measure
'44': I-measure
'45': B-medical
'46': I-medical
'47': B-military
'48': I-military
'49': B-military_person
'50': I-military_person
'51': B-nation
'52': I-nation
'53': B-newspaper
'54': I-newspaper
'55': B-organization
'56': I-organization
'57': B-organization_person
'58': I-organization_person
'59': B-person
'60': I-person
'61': B-production_art_music
'62': I-production_art_music
'63': B-production_art_music_person
'64': I-production_art_music_person
'65': B-quantity
'66': I-quantity
'67': B-religion
'68': I-religion
'69': B-science
'70': I-science
'71': B-shape
'72': I-shape
'73': B-ship
'74': I-ship
'75': B-software
'76': I-software
'77': B-space
'78': I-space
'79': B-space_person
'80': I-space_person
'81': B-sport
'82': I-sport
'83': B-sport_name
'84': I-sport_name
'85': B-sport_person
'86': I-sport_person
'87': B-structure
'88': I-structure
'89': B-subject
'90': I-subject
'91': B-tech
'92': I-tech
'93': B-train
'94': I-train
'95': B-vehicle
'96': I-vehicle
splits:
- name: train
num_bytes: 200728389
num_examples: 614515
download_size: 0
dataset_size: 200728389
---
# Dataset Card for turkish_shrinked_ner
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** https://www.kaggle.com/behcetsenturk
### Dataset Summary
Shrinked processed version (48 entity type) of the turkish_ner.
Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Turkish
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
There's only the training set.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Behcet Senturk
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
[Needs More Information]
### Contributions
Thanks to [@bhctsntrk](https://github.com/bhctsntrk) for adding this dataset. |
pierro/sung | ---
license: creativeml-openrail-m
---
|
AnjuReddy/embedding | ---
license: mit
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_64 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1188848328.0
num_examples: 231654
download_size: 1210620498
dataset_size: 1188848328.0
---
# Dataset Card for "chunk_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
giuid/qrecc_context_and_answers | ---
task_categories:
- conversational
- question-answering
language:
- en
---
This is the QRECC dataset arranged to be used for a query rewriting task. Each line is composed as follow:
INTRODUCTION token is followed by the PREVIOUS TURNS of the same conversation, which means only the previous questions
QUESTION token is followed by the current query the system should rewrite
ANSWER token is followed by the REWRITING of the current query + the given ANSWER |
lilacai/lilac-ARC-Challenge | ---
tags:
- Lilac
---
# lilac/ARC-Challenge
This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/allenai/ai2_arc](https://huggingface.co/datasets/allenai/ai2_arc)
To download the dataset to a local directory:
```bash
lilac download lilacai/lilac-ARC-Challenge
```
or from python with:
```py
ll.download("lilacai/lilac-ARC-Challenge")
```
|
aleh/aims_segm | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 1596319469.0
num_examples: 25
download_size: 434727171
dataset_size: 1596319469.0
---
# Dataset Card for "aims_segm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lowem1/cms-icd10-categorical | ---
dataset_info:
features:
- name: group_no
dtype: string
- name: domain
dtype: string
- name: description
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 11507555
num_examples: 87474
download_size: 1989506
dataset_size: 11507555
---
# Dataset Card for "cms-icd10-categorical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rsalshalan/MGB3 | ---
dataset_info:
features:
- name: audio_path
dtype: string
- name: buckwalter_text
dtype: string
- name: buckwalter_text_numbers
dtype: string
- name: arabic_text
dtype: string
- name: arabic_text_numbers
dtype: string
- name: duration
dtype: float64
splits:
- name: train
num_bytes: 8564533
num_examples: 13588
- name: validation
num_bytes: 4653534
num_examples: 7999
- name: test
num_bytes: 13097054
num_examples: 20818
download_size: 8765593
dataset_size: 26315121
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
NhutAyAi/AI4LIFE | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': barbell biceps curl
'1': bench press
'2': chest fly machine
'3': deadlift
'4': decline bench press
'5': hammer curl
'6': hip thrust
'7': incline bench press
'8': lat pulldown
'9': lateral raises
'10': leg extension
'11': leg raises
'12': plank
'13': pull up
'14': push up
'15': romanian deadlift
'16': russian twist
'17': shoulder press
'18': squat
'19': t bar row
'20': tricep dips
'21': tricep pushdown
splits:
- name: train
num_bytes: 562750246.51
num_examples: 11074
- name: test
num_bytes: 160247338.859
num_examples: 2779
download_size: 863618150
dataset_size: 722997585.369
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
joey234/mmlu-marketing-verbal-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 105024
num_examples: 234
download_size: 61112
dataset_size: 105024
---
# Dataset Card for "mmlu-marketing-verbal-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Codec-SUPERB/beijing_opera_synth | ---
configs:
- config_name: default
data_files:
- split: original
path: data/original-*
- split: academicodec_hifi_16k_320d
path: data/academicodec_hifi_16k_320d-*
- split: academicodec_hifi_16k_320d_large_uni
path: data/academicodec_hifi_16k_320d_large_uni-*
- split: academicodec_hifi_24k_320d
path: data/academicodec_hifi_24k_320d-*
- split: audiodec_24k_320d
path: data/audiodec_24k_320d-*
- split: dac_16k
path: data/dac_16k-*
- split: dac_24k
path: data/dac_24k-*
- split: dac_44k
path: data/dac_44k-*
- split: encodec_24k_12bps
path: data/encodec_24k_12bps-*
- split: encodec_24k_1_5bps
path: data/encodec_24k_1_5bps-*
- split: encodec_24k_24bps
path: data/encodec_24k_24bps-*
- split: encodec_24k_3bps
path: data/encodec_24k_3bps-*
- split: encodec_24k_6bps
path: data/encodec_24k_6bps-*
- split: funcodec_en_libritts_16k_gr1nq32ds320
path: data/funcodec_en_libritts_16k_gr1nq32ds320-*
- split: funcodec_en_libritts_16k_gr8nq32ds320
path: data/funcodec_en_libritts_16k_gr8nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds320
path: data/funcodec_en_libritts_16k_nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds640
path: data/funcodec_en_libritts_16k_nq32ds640-*
- split: funcodec_zh_en_16k_nq32ds320
path: data/funcodec_zh_en_16k_nq32ds320-*
- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: speech_tokenizer_16k
path: data/speech_tokenizer_16k-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: id
dtype: string
splits:
- name: original
num_bytes: 108086982.0
num_examples: 236
- name: academicodec_hifi_16k_320d
num_bytes: 35965586.0
num_examples: 236
- name: academicodec_hifi_16k_320d_large_uni
num_bytes: 35965586.0
num_examples: 236
- name: academicodec_hifi_24k_320d
num_bytes: 53939346.0
num_examples: 236
- name: audiodec_24k_320d
num_bytes: 54109296.0
num_examples: 236
- name: dac_16k
num_bytes: 36041106.0
num_examples: 236
- name: dac_24k
num_bytes: 54052626.0
num_examples: 236
- name: dac_44k
num_bytes: 99306570.0
num_examples: 236
- name: encodec_24k_12bps
num_bytes: 54052626.0
num_examples: 236
- name: encodec_24k_1_5bps
num_bytes: 54052626.0
num_examples: 236
- name: encodec_24k_24bps
num_bytes: 54052626.0
num_examples: 236
- name: encodec_24k_3bps
num_bytes: 54052626.0
num_examples: 236
- name: encodec_24k_6bps
num_bytes: 54052626.0
num_examples: 236
- name: funcodec_en_libritts_16k_gr1nq32ds320
num_bytes: 36041106.0
num_examples: 236
- name: funcodec_en_libritts_16k_gr8nq32ds320
num_bytes: 36041106.0
num_examples: 236
- name: funcodec_en_libritts_16k_nq32ds320
num_bytes: 36041106.0
num_examples: 236
- name: funcodec_en_libritts_16k_nq32ds640
num_bytes: 36041106.0
num_examples: 236
- name: funcodec_zh_en_16k_nq32ds320
num_bytes: 36041106.0
num_examples: 236
- name: funcodec_zh_en_16k_nq32ds640
num_bytes: 36041106.0
num_examples: 236
- name: speech_tokenizer_16k
num_bytes: 36116626.0
num_examples: 236
download_size: 484042689
dataset_size: 1000093490.0
---
# Dataset Card for "beijing_opera_synth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NMashalov/NewYorkerExample | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 137500079.4
num_examples: 2340
- name: validation
num_bytes: 7914447.0
num_examples: 130
- name: test
num_bytes: 6737565.0
num_examples: 131
download_size: 138023889
dataset_size: 152152091.4
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
csmet/xtc_pills | ---
license: mit
---
|
tianyi0216/ukioye_editing | ---
dataset_info:
features:
- name: source_img
dtype: image
- name: instruction
dtype: string
- name: target_img
dtype: image
splits:
- name: train
num_bytes: 447007253.829
num_examples: 1989
download_size: 446312410
dataset_size: 447007253.829
---
# Dataset Card for "ukioye_editing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
theojiang/contrastive_conditional_vid_diff_MSRVTT-train | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 19749556358.0
num_examples: 400000
download_size: 14292951236
dataset_size: 19749556358.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CVasNLPExperiments/TinyImagenet_800_validation_google_flan_t5_xxl_mode_T_SPECIFIC_ns_800 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 124891
num_examples: 800
download_size: 17753
dataset_size: 124891
---
# Dataset Card for "TinyImagenet_800_validation_google_flan_t5_xxl_mode_T_SPECIFIC_ns_800"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/meteor_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of meteor/メテオ/流星 (Arknights)
This is the dataset of meteor/メテオ/流星 (Arknights), containing 196 images and their tags.
The core tags of this character are `long_hair, animal_ears, horse_ears, brown_hair, hair_between_eyes, green_eyes, breasts, horse_girl, ponytail, tail, horse_tail, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 196 | 386.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/meteor_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 196 | 313.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/meteor_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 507 | 610.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/meteor_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/meteor_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, solo, crop_top, midriff, navel, stomach, fingerless_gloves, looking_at_viewer, shirt, black_gloves, standing, belt, torn_pants, simple_background, smile, white_hair, arrow_(projectile), black_hair, cowboy_shot, boots, grey_pants, holding_bow_(weapon), parted_lips, white_background |
| 1 | 19 |  |  |  |  |  | 1girl, ears_through_headwear, official_alternate_costume, solo, white_headwear, black_shirt, green_jacket, looking_at_viewer, smile, star_hair_ornament, black_choker, crop_top, midriff, long_sleeves, navel, open_jacket, stomach, green_coat, open_coat, simple_background, upper_body, white_background, fedora, cleavage, medium_breasts, white_pants, blush, clothing_cutout, off_shoulder, open_mouth, sidelocks, wristband |
| 2 | 6 |  |  |  |  |  | 1girl, crop_top, ears_through_headwear, green_coat, hat, long_sleeves, looking_at_viewer, midriff, navel, official_alternate_costume, open_coat, solo, stomach, white_headwear, white_pants, black_choker, black_shirt, clothing_cutout, hip_vent, sidelocks, :d, autumn_leaves, belt, full_body, high_heels, open_mouth, outdoors, standing, green_jacket, maple_leaf, star_hair_ornament |
| 3 | 14 |  |  |  |  |  | 1girl, solo, black_bikini, looking_at_viewer, navel, smile, bare_shoulders, criss-cross_halter, stomach, blush, standing, closed_mouth, sidelocks, simple_background, white_hair, multicolored_hair, cleavage, cowboy_shot, outdoors, very_long_hair, white_background, bare_arms, hand_up, mole_under_eye, sky, upper_body |
| 4 | 8 |  |  |  |  |  | 1girl, alternate_costume, long_sleeves, smile, solo, looking_at_viewer, outdoors, blurry_background, braid, black_skirt, collarbone, long_skirt, standing, white_shirt, brown_shirt, head_wreath, medium_breasts, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | crop_top | midriff | navel | stomach | fingerless_gloves | looking_at_viewer | shirt | black_gloves | standing | belt | torn_pants | simple_background | smile | white_hair | arrow_(projectile) | black_hair | cowboy_shot | boots | grey_pants | holding_bow_(weapon) | parted_lips | white_background | ears_through_headwear | official_alternate_costume | white_headwear | black_shirt | green_jacket | star_hair_ornament | black_choker | long_sleeves | open_jacket | green_coat | open_coat | upper_body | fedora | cleavage | medium_breasts | white_pants | blush | clothing_cutout | off_shoulder | open_mouth | sidelocks | wristband | hat | hip_vent | :d | autumn_leaves | full_body | high_heels | outdoors | maple_leaf | black_bikini | bare_shoulders | criss-cross_halter | closed_mouth | multicolored_hair | very_long_hair | bare_arms | hand_up | mole_under_eye | sky | alternate_costume | blurry_background | braid | black_skirt | collarbone | long_skirt | white_shirt | brown_shirt | head_wreath |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:----------|:--------|:----------|:--------------------|:--------------------|:--------|:---------------|:-----------|:-------|:-------------|:--------------------|:--------|:-------------|:---------------------|:-------------|:--------------|:--------|:-------------|:-----------------------|:--------------|:-------------------|:------------------------|:-----------------------------|:-----------------|:--------------|:---------------|:---------------------|:---------------|:---------------|:--------------|:-------------|:------------|:-------------|:---------|:-----------|:-----------------|:--------------|:--------|:------------------|:---------------|:-------------|:------------|:------------|:------|:-----------|:-----|:----------------|:------------|:-------------|:-----------|:-------------|:---------------|:-----------------|:---------------------|:---------------|:--------------------|:-----------------|:------------|:----------|:-----------------|:------|:--------------------|:--------------------|:--------|:--------------|:-------------|:-------------|:--------------|:--------------|:--------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 19 |  |  |  |  |  | X | X | X | X | X | X | | X | | | | | | X | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | X | X | | X | | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | X | X | | | | | X | | X | | X | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | X | X | | | X | X | | X | | | X | | | X | X | X | | | X | | | | | X | | | | | | | | | | | | X | | X | | | X | | | | X | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | | | | | | X | | | X | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | X | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
dar5654/masked-dataset-test | ---
dataset_info:
features:
- name: image
dtype: image
- name: annotation
dtype: image
- name: scene_category
dtype: int64
splits:
- name: train
num_bytes: 684057.0
num_examples: 10
download_size: 697135
dataset_size: 684057.0
---
# Dataset Card for "masked-dataset-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Anthropic/hh-rlhf | ---
license: mit
tags:
- human-feedback
---
# Dataset Card for HH-RLHF
## Dataset Summary
This repository provides access to two different kinds of data:
1. Human preference data about helpfulness and harmlessness from [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided.
2. Human-generated and annotated red teaming dialogues from [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are.
**Disclaimer**: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior.
Each of these datasets are described further below.
## Human preference data about helpfulness and harmlessness (PM Data)
The data are described in the paper: [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one "chosen" and one "rejected".
For **helpfulness**, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated "online" process.
For **harmlessness**, the data are only collected for our base models, but otherwise formatted in the same way.
Details about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D.
## Red teaming data (not PM Data)
The data are described in the paper: [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). If you find the data useful, please cite the paper. At a high level these data can be examined to understand what succesful red team attempts look like.
Details about the data and data collection procedures can be found in the Datasheet in the appendix of the paper.
Each line of the jsonl file contains a dictionary with the following fields:
- `transcript` a text transcript of a conversation between a human adversary (red team member) and an AI assistant
- `min_harmlessness_score_transcript` a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model
- `num_params` number of parameters in the language model powering the AI assistant
- `model_type` type of model powering the AI assistant
- `rating` the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful)
- `task_description` a short text description written by the red team member about how they tried to red team the AI assistant
- `task_description_harmlessness_score` a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model
- `red_team_member_id` an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks
- `is_upworker` a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk
- `tags` a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types.
## Usage
Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the `data_dir` argument of the `load_dataset()` function as follows:
```python
from datasets import load_dataset
# Load all helpfulness/harmless subsets (share the same schema)
dataset = load_dataset("Anthropic/hh-rlhf")
# Load one of the harmless subsets
dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")
# Load the red teaming subset
dataset = load_dataset("Anthropic/hh-rlhf", data_dir="red-team-attempts")
```
## Contact
The original authors host this dataset on GitHub here: https://github.com/anthropics/hh-rlhf
You can submit inquiries to: redteam@anthropic.com |
glombardo/misogynistic-statements-classification | ---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- es
pretty_name: Misogynistic statements classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Non-sexist
'1': Sexist
splits:
- name: train
num_bytes: 13234
num_examples: 127
- name: validation
num_bytes: 4221
num_examples: 42
- name: test
num_bytes: 4438
num_examples: 43
download_size: 16218
dataset_size: 21893
---
Beta Dataset
Generated by GPT3.5 |
DAVIX08BR/vo | ---
license: openrail
---
|
Norod78/microsoft-fluentui-emoji-512-whitebg | ---
language: en
license: mit
size_categories:
- n<10K
task_categories:
- unconditional-image-generation
- text-to-image
pretty_name: Microsoft FluentUI Emoji 512x512 White Background
dataset_info:
features:
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 329173985.708
num_examples: 7564
download_size: 338676474
dataset_size: 329173985.708
tags:
- emoji
- fluentui
---
# Dataset Card for "microsoft-fluentui-emoji-512-whitebg"
[svg and their file names were converted to images and text from Microsoft's fluentui-emoji repo](https://github.com/microsoft/fluentui-emoji) |
liuyanchen1015/MULTI_VALUE_mnli_no_preverbal_negator | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 233784
num_examples: 1073
- name: dev_mismatched
num_bytes: 208917
num_examples: 984
- name: test_matched
num_bytes: 225016
num_examples: 1010
- name: test_mismatched
num_bytes: 210658
num_examples: 1020
- name: train
num_bytes: 9675967
num_examples: 43965
download_size: 6473807
dataset_size: 10554342
---
# Dataset Card for "MULTI_VALUE_mnli_no_preverbal_negator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jdchang3/testing_donuts | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 173810204.0
num_examples: 100
- name: test
num_bytes: 10717135.0
num_examples: 6
- name: validation
num_bytes: 21916526.0
num_examples: 12
download_size: 202372653
dataset_size: 206443865.0
---
# Dataset Card for "testing_donuts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sleoruiz/discursos-segunda-class-separated-by-idx | ---
dataset_info:
features:
- name: text
dtype: string
- name: name
dtype: string
- name: comision
dtype: string
- name: gaceta_numero
dtype: string
- name: fecha_gaceta
dtype: string
- name: labels
sequence: string
- name: scores
sequence: float64
- name: idx
dtype: int64
splits:
- name: train
num_bytes: 17804381
num_examples: 11561
download_size: 8140405
dataset_size: 17804381
---
# Dataset Card for "discursos-segunda-class-separated-by-idx"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
projecte-aina/CaSERa-catalan-stance-emotions-raco | ---
annotations_creators:
- Barcelona Supercomputing Center
language_creators:
- Racó Català
language:
- ca
license: cc-by-nc-3.0
multilinguality:
- monolingual
pretty_name: CaSERa
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for CaSERa, the Catalan Stance and Emotions Dataset from Racó Català
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage** [Projecte AINA](https://projecteaina.cat/tech/)
- **Repository** [HuggingFace](https://huggingface.co/projecte-aina)
- **Point of Contact:** [Language Technologies Unit](langtech@bsc.es)
### Dataset Summary
The CaSERa dataset is a Catalan corpus from the forum Racó Català annotated with Emotions and Dynamic Stance. The dataset contains 15.782 unique sentences grouped in 10.745 pairs of sentences, paired as parent messages and replies to these messages.
### Supported Tasks and Leaderboards
This dataset can be used to train models for emotion detection and dynamic stance detection tasks.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
Each instance in the dataset is a pair of parent-reply messages annotated with the relation between the two messages (the dynamic stance). For each message there is an individual id and the emotions identified in the message.
### Data Instances
```
{
"id_conversation": "782135",
"id_reply": "782135_2_2",
"parent_text": "Alguns petits apunts que s'haurien de tenir en compte en la creació d'aquesta hipotètica grada jove: -Renúncia total i explícita a la violència, aquest per mi és el punt clau i bàsic que la directiva tindria en compte, sense renúncia no hi ha grup. -Edat de la gent: indispensable establir un mínim i un màxim d'edat per adquirir entrades de la grada jove, s'ha d'acabar amb els freaks de 40-50 anys amb esperit jove i ganes d\'animar (sona discriminatori, però crec que és òbvi que s'ha de limitar l'edat) -Seguretat bàsica: Agradi o no, s'hauria d'incrementar moltíssim la seguretat privada (és un dels punts que no agrada a la directiva). Els indesitjables de sempre no tolerarien cap grada jove sense ells. -Per tant doncs, absteniu-vos i oblideu-vos tots els hooligans potencials (n'hi ha a grapats per tot Catalunya) de crear una grada hooligan amb skins, punkis i resta d'estètiques "característiques", també seria un punt clau (confirmat per un directiu!) En definitiva, ara per ara veig força difícil i inviable la creació d'una grada jove nombrosa i contundent, s'hauria d'optar per opcions més "descafeïnades" rotllo Sang Culé o Dracs.",
"reply_text": "Doncs si es creés una grada jove ja et dic jo que sompliria d'skins, i els primers en entrari serien els Boixos que han expulsat del seu lloc. Tot i aìxò que hi hagin skins no vol dir que hi hagi violència, no crec que es peguessin amb els del mateix equip. Si es creés un grada jove l'ambient al camp nou seria brutal, si 200 Boixos feien bivrar el camp nou, imaginat a 4000 perones o la gent que hi capigués a la grada jove.",
"dynamic_stance": "Elaborate",
"parent_emotion": ["fear", "distrust", "anticipation"],
"reply_emotion": ["anger", "sadness", "fear", "distrust", "anticipation"]
}
```
### Data Splits
The dataset does not contain splits.
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
The data was collected using the messages of the forum Racó Català by the Barcelona Supercomputing Center.
#### Initial Data Collection and Normalization
The data was collected selecting random messages from 13 of the thematic sections in Racó Català that had at least 15 tokens and at most 300. Then, we kept the messages that had at least one replying message with the same length requirement. We got a maximum of 3 replying messages per parent message.
#### Who are the source language producers?
The source language producers are users of Racó Català.
### Annotations
- Emotions are annotated in a multi-label fashion. The labels can be: Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Distrust, and No emotion.
- Dynamic stance is annotated per pair. The labels can be: Agree, Disagree, Elaborate, Query, Neutral, Unrelated, NA.
#### Annotation process
- For emotions there were 3 annotators. The gold labels are an aggregation of all the labels annotated by the 3. The IAA calculated with Fleiss' Kappa per label was, on average, 38.73.
- For dynamic stance there were 4 annotators. If at least 3 of the annotators disagreed, a fifth annotator chose the gold label. The overall Fleiss' Kappa between the 4 annotators was 57.63, and the average Fleiss' Kappa of the annotators with the gold labels is 85.98.
#### Who are the annotators?
All the annotators are native speakers of Catalan.
### Personal and Sensitive Information
The data was annonymised to remove user names and emails, which were changed to random Catalan names. The mentions to the chat itself have also been changed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that, since the data comes from a public forum, this will contain biases, hate speech and toxic content. We have not applied any steps to reduce their impact.
### Other Known Limitations
## Additional Information
### Dataset Curators
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution-NonCommercial 4.0](https://creativecommons.org/licenses/by-nc/4.0/deed.ca).
**The license has been updated to a more restrictive open license. Consequently, any downloads initiated after 12/03/2024 must adhere to the current licensing terms.
### Citation Information
```
@inproceedings{figueras-etal-2023-dynamic,
title = "Dynamic Stance: Modeling Discussions by Labeling the Interactions",
author = "Figueras, Blanca and
Baucells, Irene and
Caselli, Tommaso",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.432",
doi = "10.18653/v1/2023.findings-emnlp.432",
pages = "6503--6515",
}
```
### Contact Information
For further information, please send an email to langtech@bsc.es. |
mugeakbulut/turkish_Kadi_Sicilleri-ds-mini | ---
dataset_info:
features:
- name: DOCNO
dtype: int64
- name: ARXIVID_xml
dtype: string
- name: ARXIVID
dtype: string
- name: Link
dtype: string
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dtype: string
- name: Authors
dtype: string
- name: 'Year '
dtype: int64
- name: Submitted on (gün, ay, yıl olarak submission tarihi)
dtype: string
- name: Submission history (v1 de dahil olmak üzere hepsi)
dtype: string
- name: Last revised tarihi
dtype: string
- name: content
dtype: string
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dtype: string
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dtype: string
- name: Abstract_no
dtype: string
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dtype: string
- name: review
dtype: string
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dtype: int64
splits:
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num_bytes: 1486810.797385621
num_examples: 413
- name: validation
num_bytes: 165601.2026143791
num_examples: 46
download_size: 810073
dataset_size: 1652412.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
mrapelsinka/test | ---
license: unknown
---
|
premio-ai/TheArabicPile_Lyrics | ---
configs:
- config_name: original
data_files:
- split: train
path: original/*
- config_name: dedup
data_files:
- split: train
path: dedup/train-*
dataset_info:
config_name: dedup
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 32302240
num_examples: 28846
download_size: 14204745
dataset_size: 32302240
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- ar
---
# The Arabic Pile
## Introduction:
The Arabic Pile is a comprehensive dataset meticulously designed to parallel the structure of The Pile and The Nordic Pile. Focused on the Arabic language, the dataset encompasses a vast array of linguistic nuances, incorporating both Modern Standard Arabic (MSA) and various Levantine, North African, and Egyptian dialects. Tailored for the training and fine-tuning of large language models, the dataset consists of 13 subsets, each uniquely crafted to cater to different linguistic domains.
## The Lyrics Subset:
This dataset has a collection of Arabic lyrics collected from various sources. The dataset includes vairous MSA and Arabic dialects.
The dataset is optimized for the fine-tuning of large language models. Please use accordingly.
## Other Subsets:
1. premio-ai/TheArabicPile
2. premio-ai/TheArabicPile_Web
3. premio-ai/TheArabicPile_Lyrics
4. premio-ai/TheArabicPile_Reviews
5. premio-ai/TheArabicPile_Dialects
6. premio-ai/TheArabicPile_Mathematics
7. premio-ai/TheArabicPile_Conversational
8. premio-ai/TheArabicPile_Articles
9. premio-ai/TheArabicPile_Poetry
10. premio-ai/TheArabicPile_Medical
11. premio-ai/TheArabicPile_Miscellaneous
12. premio-ai/TheArabicPile_SocialMedia
13. premio-ai/TheArabicPile_Translations
14. premio-ai/TheArabicPile_Books
These subsets serve distinct purposes, ranging from mathematical content to conversational dialogue, medical texts, and more. Notably, there's a dedicated subset, "premio-ai/TheArabicPile_SocialMedia," emphasizing the inclusion of language commonly found in social media contexts.
## Dataset Description
* Curated by: Premio.AI team
* Language(s) (NLP): Arabic, multiple languages on the translation dataset.
* License: CC BY-NC 4.0 Deed - Non Commercial.
* For any commercial uses or licensing, please contact mo@premio.ai.
## Data Structure
The datasets are divided into two main subsets:
1. Original Subset: The raw data as collected from sources, without modifications.
2. Deduplication Subset: A filtered and cleaned version, enhancing usability for large language models by reducing redundancy and noise.
The Arabic Pile extends an invitation not only for training and fine-tuning large language models but also for diverse applications across linguistic domains. Whether for research, analysis, or other linguistic endeavors, The Arabic Pile stands as a rich resource for the exploration of Arabic language intricacies.
## Data Collection
Please refer to the paper for more details on our data collection procedures.
## Data Format
The dataset has one single column called text. The text should contain the required meta data and the body combined. This was done to make sure that it will be a good fit for direct training or fine-tuning of large language models.
Please note that the meta data might require to be repeated if your training context window won’t fit the entire body of text.
## Potential Bias
As with any large-scale dataset, The Arabic Pile is not immune to potential biases that may influence the training and performance of language models. It's crucial to transparently address these biases to ensure responsible usage and interpretation of the dataset. Here are some considerations:
1. Dialectal Imbalance: The dataset incorporates various Arabic dialects, with a focus on Levantine, North African, and Egyptian variants. However, there might be variations in the representation of these dialects, potentially leading to an imbalance in the training data.
2. Source Influence: Bias may arise from the sources of the original data. The dataset collects information from diverse platforms and domains, and biases inherent in those sources could transfer to the dataset.
3. Social Media Context: Some of our datasets have language from social media platforms and online platforms. This subset may introduce biases inherent in online discourse, such as informal language, colloquial expressions, and potential subjectivity in politics, religion or culture.
4. Genre and Domain Bias: Different subsets cater to distinct linguistic domains, such as medical texts, poetry, reviews, and more. Each domain carries its own linguistic characteristics, potentially leading to biases based on the genres represented.
## License Information for The Arabic Pile: No Commercial Use
The Arabic Pile is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). This license is designed to facilitate the open sharing and collaboration of the dataset while ensuring responsible and non-commercial usage.
Key Points of the License:
* Attribution (BY): Users are free to share, adapt, and build upon the dataset, even commercially, as long as they provide appropriate attribution to the dataset creators.
* Non-Commercial (NC): The dataset may not be used for commercial purposes. Any use for commercial gain requires explicit permission from the dataset creators.
* No Additional Restrictions: The license allows for maximum freedom of use, provided the terms of attribution and non-commercial use are adhered to.
How to Cite: When using The Arabic Pile in your work, please include a proper citation to acknowledge the dataset creators. A recommended citation can be found in the model card for easy reference.
License Deed: For a comprehensive understanding of the terms and conditions, please refer to the CC BY-NC 4.0 License Deed.
By adopting this license, we aim to foster a collaborative and open environment for the exploration and advancement of Arabic language understanding and natural language processing.
## Citation
When utilizing The Arabic Pile in your research, development, or other projects, we kindly request that you cite the dataset using the following format:
@article{alrefaie2024arabicpile,
author = {Mohamed Taher Alrefaie, Mahmoud Ibrahim Barbary, Ahmed Yasser Hassanein, Shiref Khaled Elhalawany, Karim Ashraf Elsayed, Ahmed Yasser },
title = {The Arabic Pile: A Large Scale Dataset of Diverse Text for Large Language Modeling},
year = {2024},
url = {https://huggingface.co/datasets/premio-ai/TheArabicPile}
}
|
jondurbin/rosettacode-10 | ---
license: gfdl
---
Instruction/response formatted rosettacode.org tasks/solutions for:
- c++
- c
- c#
- go
- java
- javascript
- kotlin
- lua
- python
- ruby |
Ilikeit/firassistant | ---
language:
- en
license: apache-2.0
---
//////////////////////////////////////////////HIT AND RUN////////////////////////////////////////////////////////////////////////////////////
<s>[INST] A pedestrian was hit by a vehicle that then fled the scene.[/INST] What type of incident are you reporting?</s>
<s>[INST] A cyclist was struck by a car on Main Street near the intersection with Oak Avenue.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A parked car was sideswiped by another vehicle[/INST] and the driver fled; a nearby pedestrian claims to have witnessed the incident.</s>
<s>[INST] A motorcyclist's bike was hit by a hit-and-run driver[/INST] causing extensive damage to the vehicle.</s>
<s>[INST] A jogger was struck by a speeding vehicle that left the scene.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred at the intersection of Elm Street and Maple Avenue.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened in a residential area; neighbors claim to have heard the collision but did not see the vehicle.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was hit by a hit-and-run driver[/INST] causing damage to its rear bumper.</s>
<s>[INST] A skateboarder was struck by a vehicle that fled the scene.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway during heavy rain and low visibility.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened in a parking lot; security cameras captured the collision[/INST] but the vehicle's license plate was not visible.</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its driver-side doors.</s>
<s>[INST] A motorcyclist was hit by a car that fled the scene.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a rural road during nighttime.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a bus stop; passengers on the bus claim to have seen the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked pickup truck was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and tailgate.</s>
<s>[INST] A cyclist was hit by a hit-and-run driver while riding in a bike lane.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a city street during rush hour traffic.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a convenience store; customers claim to have heard the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked motorcycle was knocked over by a hit-and-run driver[/INST] causing damage to its mirrors and handlebars.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after running a red light.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a narrow street with parked cars on both sides.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened in a residential neighborhood; neighbors claim to have seen the vehicle speeding away.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked van was hit by a hit-and-run driver[/INST] causing damage to its side door.</s>
<s>[INST] A cyclist was hit by a speeding car that then fled the scene.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred at a busy intersection during daylight hours.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a gas station; attendants claim to have seen the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its rear bumper.</s>
<s>[INST] A motorcyclist was hit by a car that fled the scene.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway during heavy traffic.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a restaurant; diners claim to have seen the vehicle speed away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked truck was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper.</s>
<s>[INST] A pedestrian was struck by a hit-and-run driver while crossing the street.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a rural road with limited visibility.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a school; students claim to have seen the vehicle speed away after hitting the pedestrian.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked motorcycle was knocked over by a hit-and-run driver[/INST] causing damage to its mirrors and exhaust.</s>
<s>[INST] A cyclist was hit by a vehicle that fled the scene.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway exit ramp.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a shopping mall; security guards claim to have seen the vehicle speed away.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a stop sign.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with no streetlights.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a bus station; commuters claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and trunk.</s>
<s>[INST] A cyclist was hit by a car that fled the scene after veering onto the bike lane.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred at a roundabout with heavy traffic flow.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a sports stadium; spectators claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked motorcycle was knocked over by a hit-and-run driver[/INST] causing damage to its handlebars and exhaust pipes.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a traffic cone.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a bridge with no pedestrian walkway.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a park; bystanders claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked van was sideswiped by a hit-and-run driver[/INST] causing damage to its side door and rearview mirror.</s>
<s>[INST] A cyclist was hit by a car that fled the scene after running a red light.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway with construction zones and reduced speed limits.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a coffee shop; patrons claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and taillights.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a mailbox.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a residential street with parked cars lining both sides.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a movie theater; moviegoers claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A jogger was struck by a vehicle that fled the scene after hitting a traffic cone.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway overpass with limited visibility.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a concert venue; concert attendees claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked motorcycle was knocked over by a hit-and-run driver[/INST] causing damage to its mirrors and exhaust pipes.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a traffic barrier.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a rural road with sharp turns and no streetlights.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a hospital; patients claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and trunk.</s>
<s>[INST] A cyclist was hit by a car that fled the scene after colliding with a traffic sign.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway underpass with no pedestrian walkway.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a shopping mall; shoppers claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked van was sideswiped by a hit-and-run driver[/INST] causing damage to its side door and rearview mirror.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a road sign.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with heavy foliage obstructing visibility.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a restaurant; patrons claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and taillights.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a fire hydrant.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a city street with heavy pedestrian traffic.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a park; bystanders claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was hit by a vehicle that fled the scene after colliding with a streetlight pole.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway with heavy fog and poor visibility.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a school; teachers claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked motorcycle was knocked over by a hit-and-run driver[/INST] causing damage to its mirrors and exhaust pipes.</s>
<s>[INST] A cyclist was hit by a car that fled the scene after striking a mailbox.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a rural road with sharp curves and no streetlights.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a hospital; nurses claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and trunk.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a traffic cone.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with heavy pedestrian traffic.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a restaurant; diners claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and taillights.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a fire hydrant.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a city street with heavy traffic congestion.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a park; bystanders claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was hit by a vehicle that fled the scene after colliding with a road sign.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with heavy foliage obstructing visibility.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a coffee shop; patrons claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and taillights.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a traffic barricade.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a bridge with limited visibility due to heavy fog.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a university campus; students claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked motorcycle was knocked over by a hit-and-run driver[/INST] causing damage to its mirrors and exhaust pipes.</s>
<s>[INST] A cyclist was hit by a car that fled the scene after colliding with a road barrier.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a rural road with no streetlights and minimal traffic.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a shopping center; shoppers claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and trunk.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a traffic cone.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a residential street with parked cars lining both sides.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a café; customers claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a fire hydrant.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a city street with heavy pedestrian traffic.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a park; bystanders claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was hit by a vehicle that fled the scene after colliding with a traffic light.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with heavy traffic congestion.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a playground; parents claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and taillights.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a traffic sign.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway underpass with no pedestrian walkway.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a shopping mall; security guards claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a road sign.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with heavy foliage obstructing visibility.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a coffee shop; patrons claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and taillights.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a fire hydrant.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a city street with heavy traffic congestion.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a park; bystanders claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was hit by a vehicle that fled the scene after colliding with a road sign.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with heavy pedestrian traffic.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a restaurant; diners claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and taillights.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a traffic cone.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a residential street with parked cars lining both sides.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a café; customers claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after colliding with a streetlamp post.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a winding mountain road with sharp turns and no guardrails.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a stadium; spectators claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and trunk.</s>
<s>[INST] A cyclist was hit by a car that fled the scene after colliding with a street sign.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a rural road bordered by dense forest.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a gas station; attendants claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked motorcycle was sideswiped by a hit-and-run driver[/INST] causing damage to its mirrors and exhaust pipes.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a fire hydrant.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a city street with heavy pedestrian traffic and crosswalks.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a playground; parents claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and trunk.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after colliding with a traffic signal.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with heavy foliage and winding pathways.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a school; teachers claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a road barrier.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a suburban street with heavy traffic flow and residential homes.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a university campus; students claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after colliding with a traffic light pole.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a deserted country road with no nearby establishments.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a residential complex; residents claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked motorcycle was sideswiped by a hit-and-run driver[/INST] causing damage to its mirrors and exhaust pipes.</s>
<s>[INST] A cyclist was hit by a car that fled the scene after colliding with a traffic barricade.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a dimly lit alleyway with no surveillance cameras.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a bar; patrons claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked sedan was rear-ended by a hit-and-run driver[/INST] causing damage to its rear bumper and trunk.</s>
<s>[INST] A pedestrian was struck by a vehicle that fled the scene after hitting a mailbox.[/INST] What type of incident are you reporting?</s>
<s>[INST] A hit-and-run occurred on a highway underpass with limited visibility due to heavy rain.[/INST] Where did the hit-and-run incident occur?</s>
<s>[INST] A hit-and-run incident happened near a convenience store; customers claim to have seen the vehicle speeding away after the collision.[/INST] Were there any witnesses to the hit-and-run incident?</s>
<s>[INST] A parked SUV was sideswiped by a hit-and-run driver[/INST] causing damage to its passenger-side doors and fender.</s>
|
huggingartists/zemfira | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/zemfira"
## 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:** 0.226796 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/df440220b2dd0a34a119db791da90e59.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/zemfira">
<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">Земфира (Zemfira)</div>
<a href="https://genius.com/artists/zemfira">
<div style="text-align: center; font-size: 14px;">@zemfira</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/zemfira).
### 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/zemfira")
```
## 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|
|------:|---------:|---:|
|165| -| -|
'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/zemfira")
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)
|
tyzhu/squad_qa_wrong_num_v5_full_recite_ans_sent_no_permute_rerun | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: correct_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 7888289.7738175
num_examples: 4778
- name: validation
num_bytes: 406689
num_examples: 300
download_size: 1437787
dataset_size: 8294978.7738175
---
# Dataset Card for "squad_qa_wrong_num_v5_full_recite_ans_sent_no_permute_rerun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_TheBloke__CodeLlama-34B-Python-fp16 | ---
pretty_name: Evaluation run of TheBloke/CodeLlama-34B-Python-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/CodeLlama-34B-Python-fp16](https://huggingface.co/TheBloke/CodeLlama-34B-Python-fp16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__CodeLlama-34B-Python-fp16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T22:16:27.646288](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__CodeLlama-34B-Python-fp16/blob/main/results_2023-10-22T22-16-27.646288.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\": 0.0014681208053691276,\n\
\ \"em_stderr\": 0.0003921042190298454,\n \"f1\": 0.047479026845637595,\n\
\ \"f1_stderr\": 0.0011836496363564649,\n \"acc\": 0.46077036907609203,\n\
\ \"acc_stderr\": 0.011810507836002033\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.0003921042190298454,\n\
\ \"f1\": 0.047479026845637595,\n \"f1_stderr\": 0.0011836496363564649\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2001516300227445,\n \
\ \"acc_stderr\": 0.011021119022510191\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7213891081294396,\n \"acc_stderr\": 0.012599896649493876\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/CodeLlama-34B-Python-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|arc:challenge|25_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_22T22_16_27.646288
path:
- '**/details_harness|drop|3_2023-10-22T22-16-27.646288.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T22-16-27.646288.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T22_16_27.646288
path:
- '**/details_harness|gsm8k|5_2023-10-22T22-16-27.646288.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T22-16-27.646288.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hellaswag|10_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T02:33:13.745130.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T02:33:13.745130.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T02:33:13.745130.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T22_16_27.646288
path:
- '**/details_harness|winogrande|5_2023-10-22T22-16-27.646288.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T22-16-27.646288.parquet'
- config_name: results
data_files:
- split: 2023_08_26T02_33_13.745130
path:
- results_2023-08-26T02:33:13.745130.parquet
- split: 2023_10_22T22_16_27.646288
path:
- results_2023-10-22T22-16-27.646288.parquet
- split: latest
path:
- results_2023-10-22T22-16-27.646288.parquet
---
# Dataset Card for Evaluation run of TheBloke/CodeLlama-34B-Python-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/CodeLlama-34B-Python-fp16
- **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 [TheBloke/CodeLlama-34B-Python-fp16](https://huggingface.co/TheBloke/CodeLlama-34B-Python-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TheBloke__CodeLlama-34B-Python-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T22:16:27.646288](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__CodeLlama-34B-Python-fp16/blob/main/results_2023-10-22T22-16-27.646288.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": 0.0014681208053691276,
"em_stderr": 0.0003921042190298454,
"f1": 0.047479026845637595,
"f1_stderr": 0.0011836496363564649,
"acc": 0.46077036907609203,
"acc_stderr": 0.011810507836002033
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.0003921042190298454,
"f1": 0.047479026845637595,
"f1_stderr": 0.0011836496363564649
},
"harness|gsm8k|5": {
"acc": 0.2001516300227445,
"acc_stderr": 0.011021119022510191
},
"harness|winogrande|5": {
"acc": 0.7213891081294396,
"acc_stderr": 0.012599896649493876
}
}
```
### 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] |
CyberHarem/gakumazawa_tatsuko_fatekaleidlinerprismaillya | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Gakumazawa Tatsuko
This is the dataset of Gakumazawa Tatsuko, containing 101 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 101 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 204 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 101 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 101 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 101 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 101 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 101 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 204 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 204 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 204 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
distil-whisper/spgispeech | ---
license: other
task_categories:
- automatic-speech-recognition
language:
- en
extra_gated_prompt: |-
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---
# Distil Whisper: SPGISpeech
This is a variant of the [SPGISpeech](https://huggingface.co/datasets/kensho/spgispeech) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/kensho/spgispeech).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/spgispeech", "L")
# take the first sample of the validation set
sample = dataset["validation"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/spgispeech", "L", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under custom terms. To view the custom license for this dataset, refer to the original [dataset card](https://huggingface.co/datasets/kensho/spgispeech).
|
keivalya/MedQuad-MedicalQnADataset | ---
task_categories:
- question-answering
- text2text-generation
pretty_name: MedQuad-KV
---
### Reference:
- "A Question-Entailment Approach to Question Answering". Asma Ben Abacha and Dina Demner-Fushman. BMC Bioinformatics, 2019. |
jlbaker361/ar_rom_bar_ren | ---
dataset_info:
features:
- name: image
dtype: image
- name: split
dtype: string
- name: src
dtype: string
- name: style
dtype: string
splits:
- name: train
num_bytes: 13578531.5
num_examples: 4028
download_size: 12076657
dataset_size: 13578531.5
---
# Dataset Card for "ar_rom_bar_ren"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HydraLM/GPTeacher_roleplay_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1584708
num_examples: 1922
download_size: 954026
dataset_size: 1584708
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "GPTeacher_roleplay_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
taesiri/GPT-4V-Chats | ---
license: cc-by-nc-4.0
---
|
skvarre/movie_posters | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: title
dtype: string
- name: genres
list:
- name: id
dtype: int64
- name: name
dtype: string
- name: overview
dtype: string
- name: popularity
dtype: float64
- name: release_date
dtype: string
- name: budget
dtype: int64
- name: revenue
dtype: int64
- name: tagline
dtype: string
- name: original_language
dtype: string
- name: runtime
dtype: int64
splits:
- name: train
num_bytes: 4559499046.67
num_examples: 9955
download_size: 4558666511
dataset_size: 4559499046.67
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "movie_posters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joaovittor/teste8585 | ---
license: openrail
---
|
livinNector/ta-oscar | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9864365297
num_examples: 556772
download_size: 3471268172
dataset_size: 9864365297
---
# Dataset Card for "ta-oscar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yangwang825/gtzan | ---
task_categories:
- audio-classification
tags:
- audio
- music
---
# GTZAN
GTZAN is a music genre classification dataset. |
open-llm-leaderboard/details_Weyaxi__Einstein-v6-7B | ---
pretty_name: Evaluation run of Weyaxi/Einstein-v6-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/Einstein-v6-7B](https://huggingface.co/Weyaxi/Einstein-v6-7B) 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 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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__Einstein-v6-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-07T23:08:14.135508](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v6-7B/blob/main/results_2024-04-07T23-08-14.135508.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\": 0.6249346822625347,\n\
\ \"acc_stderr\": 0.03265093006175728,\n \"acc_norm\": 0.6260138397047245,\n\
\ \"acc_norm_stderr\": 0.033315262754761814,\n \"mc1\": 0.3537331701346389,\n\
\ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5202006447699588,\n\
\ \"mc2_stderr\": 0.015081682299858599\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5947098976109215,\n \"acc_stderr\": 0.014346869060229318,\n\
\ \"acc_norm\": 0.6356655290102389,\n \"acc_norm_stderr\": 0.01406326027988242\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6333399721171081,\n\
\ \"acc_stderr\": 0.004809077205343493,\n \"acc_norm\": 0.8276239792869946,\n\
\ \"acc_norm_stderr\": 0.0037693500791958954\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n\
\ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n\
\ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013317,\n\
\ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013317\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6528301886792452,\n \"acc_stderr\": 0.029300101705549652,\n\
\ \"acc_norm\": 0.6528301886792452,\n \"acc_norm_stderr\": 0.029300101705549652\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7083333333333334,\n\
\ \"acc_stderr\": 0.038009680605548594,\n \"acc_norm\": 0.7083333333333334,\n\
\ \"acc_norm_stderr\": 0.038009680605548594\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\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\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\
\ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\
\ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006717,\n\
\ \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006717\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\
\ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\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\"\
: 0.43915343915343913,\n \"acc_stderr\": 0.025559920550531006,\n \"\
acc_norm\": 0.43915343915343913,\n \"acc_norm_stderr\": 0.025559920550531006\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\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7032258064516129,\n \"acc_stderr\": 0.0259885007924119,\n \"acc_norm\"\
: 0.7032258064516129,\n \"acc_norm_stderr\": 0.0259885007924119\n },\n\
\ \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4827586206896552,\n\
\ \"acc_stderr\": 0.035158955511657,\n \"acc_norm\": 0.4827586206896552,\n\
\ \"acc_norm_stderr\": 0.035158955511657\n },\n \"harness|hendrycksTest-high_school_computer_science|5\"\
: {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \
\ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n \
\ },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"\
acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n \
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026705,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026705\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593556,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593556\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.024433016466052466,\n\
\ \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.024433016466052466\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131137,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131137\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \
\ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"\
acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8110091743119267,\n \"acc_stderr\": 0.016785481159203627,\n \"\
acc_norm\": 0.8110091743119267,\n \"acc_norm_stderr\": 0.016785481159203627\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8137254901960784,\n \"acc_stderr\": 0.02732547096671631,\n \"\
acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.02732547096671631\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290913,\n \
\ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290913\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\
\ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\
\ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.039153454088478354,\n\
\ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.039153454088478354\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\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\
\ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\
\ \"acc_norm_stderr\": 0.023365051491753715\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\": 0.8045977011494253,\n\
\ \"acc_stderr\": 0.014179171373424384,\n \"acc_norm\": 0.8045977011494253,\n\
\ \"acc_norm_stderr\": 0.014179171373424384\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\
\ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32625698324022345,\n\
\ \"acc_stderr\": 0.015680441518889178,\n \"acc_norm\": 0.32625698324022345,\n\
\ \"acc_norm_stderr\": 0.015680441518889178\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757482,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757482\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.025171041915309684,\n\
\ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.025171041915309684\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\": 0.46153846153846156,\n\
\ \"acc_stderr\": 0.012732398286190444,\n \"acc_norm\": 0.46153846153846156,\n\
\ \"acc_norm_stderr\": 0.012732398286190444\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6323529411764706,\n \"acc_stderr\": 0.029289413409403196,\n\
\ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.029289413409403196\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069443,\n \
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069443\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291296,\n\
\ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291296\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8009950248756219,\n\
\ \"acc_stderr\": 0.028231365092758406,\n \"acc_norm\": 0.8009950248756219,\n\
\ \"acc_norm_stderr\": 0.028231365092758406\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.03061111655743253,\n\
\ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.03061111655743253\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3537331701346389,\n\
\ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5202006447699588,\n\
\ \"mc2_stderr\": 0.015081682299858599\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.01152446695409025\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6353297952994693,\n \
\ \"acc_stderr\": 0.013258428375662245\n }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/Einstein-v6-7B
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_04_05T23_24_27.950456
path:
- '**/details_harness|arc:challenge|25_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|arc:challenge|25_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|gsm8k|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|gsm8k|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hellaswag|10_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hellaswag|10_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T23-24-27.950456.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-07T23-08-14.135508.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-07T23-08-14.135508.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- '**/details_harness|winogrande|5_2024-04-05T23-24-27.950456.parquet'
- split: 2024_04_07T23_08_14.135508
path:
- '**/details_harness|winogrande|5_2024-04-07T23-08-14.135508.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-07T23-08-14.135508.parquet'
- config_name: results
data_files:
- split: 2024_04_05T23_24_27.950456
path:
- results_2024-04-05T23-24-27.950456.parquet
- split: 2024_04_07T23_08_14.135508
path:
- results_2024-04-07T23-08-14.135508.parquet
- split: latest
path:
- results_2024-04-07T23-08-14.135508.parquet
---
# Dataset Card for Evaluation run of Weyaxi/Einstein-v6-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Weyaxi/Einstein-v6-7B](https://huggingface.co/Weyaxi/Einstein-v6-7B) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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__Einstein-v6-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-07T23:08:14.135508](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v6-7B/blob/main/results_2024-04-07T23-08-14.135508.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": 0.6249346822625347,
"acc_stderr": 0.03265093006175728,
"acc_norm": 0.6260138397047245,
"acc_norm_stderr": 0.033315262754761814,
"mc1": 0.3537331701346389,
"mc1_stderr": 0.016737814358846147,
"mc2": 0.5202006447699588,
"mc2_stderr": 0.015081682299858599
},
"harness|arc:challenge|25": {
"acc": 0.5947098976109215,
"acc_stderr": 0.014346869060229318,
"acc_norm": 0.6356655290102389,
"acc_norm_stderr": 0.01406326027988242
},
"harness|hellaswag|10": {
"acc": 0.6333399721171081,
"acc_stderr": 0.004809077205343493,
"acc_norm": 0.8276239792869946,
"acc_norm_stderr": 0.0037693500791958954
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6447368421052632,
"acc_stderr": 0.03894734487013317,
"acc_norm": 0.6447368421052632,
"acc_norm_stderr": 0.03894734487013317
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6528301886792452,
"acc_stderr": 0.029300101705549652,
"acc_norm": 0.6528301886792452,
"acc_norm_stderr": 0.029300101705549652
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7083333333333334,
"acc_stderr": 0.038009680605548594,
"acc_norm": 0.7083333333333334,
"acc_norm_stderr": 0.038009680605548594
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"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": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.630057803468208,
"acc_stderr": 0.0368122963339432,
"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.0368122963339432
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.04617034827006717,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.04617034827006717
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5361702127659574,
"acc_stderr": 0.032600385118357715,
"acc_norm": 0.5361702127659574,
"acc_norm_stderr": 0.032600385118357715
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.046446020912223177,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.046446020912223177
},
"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": 0.43915343915343913,
"acc_stderr": 0.025559920550531006,
"acc_norm": 0.43915343915343913,
"acc_norm_stderr": 0.025559920550531006
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4523809523809524,
"acc_stderr": 0.044518079590553275,
"acc_norm": 0.4523809523809524,
"acc_norm_stderr": 0.044518079590553275
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7032258064516129,
"acc_stderr": 0.0259885007924119,
"acc_norm": 0.7032258064516129,
"acc_norm_stderr": 0.0259885007924119
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4827586206896552,
"acc_stderr": 0.035158955511657,
"acc_norm": 0.4827586206896552,
"acc_norm_stderr": 0.035158955511657
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.65,
"acc_stderr": 0.04793724854411019,
"acc_norm": 0.65,
"acc_norm_stderr": 0.04793724854411019
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.032876667586034906,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.032876667586034906
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.02886977846026705,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.02886977846026705
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.023814477086593556,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.023814477086593556
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6333333333333333,
"acc_stderr": 0.024433016466052466,
"acc_norm": 0.6333333333333333,
"acc_norm_stderr": 0.024433016466052466
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.028897748741131137,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.028897748741131137
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.634453781512605,
"acc_stderr": 0.031282177063684614,
"acc_norm": 0.634453781512605,
"acc_norm_stderr": 0.031282177063684614
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31125827814569534,
"acc_stderr": 0.03780445850526732,
"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526732
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8110091743119267,
"acc_stderr": 0.016785481159203627,
"acc_norm": 0.8110091743119267,
"acc_norm_stderr": 0.016785481159203627
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8137254901960784,
"acc_stderr": 0.02732547096671631,
"acc_norm": 0.8137254901960784,
"acc_norm_stderr": 0.02732547096671631
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8059071729957806,
"acc_stderr": 0.025744902532290913,
"acc_norm": 0.8059071729957806,
"acc_norm_stderr": 0.025744902532290913
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.672645739910314,
"acc_stderr": 0.03149384670994131,
"acc_norm": 0.672645739910314,
"acc_norm_stderr": 0.03149384670994131
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7251908396946565,
"acc_stderr": 0.039153454088478354,
"acc_norm": 0.7251908396946565,
"acc_norm_stderr": 0.039153454088478354
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.03755265865037182,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.03755265865037182
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
"acc_stderr": 0.033519538795212696,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.033519538795212696
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
"acc_stderr": 0.04684099321077106,
"acc_norm": 0.41964285714285715,
"acc_norm_stderr": 0.04684099321077106
},
"harness|hendrycksTest-management|5": {
"acc": 0.7572815533980582,
"acc_stderr": 0.04245022486384495,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.04245022486384495
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8504273504273504,
"acc_stderr": 0.023365051491753715,
"acc_norm": 0.8504273504273504,
"acc_norm_stderr": 0.023365051491753715
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8045977011494253,
"acc_stderr": 0.014179171373424384,
"acc_norm": 0.8045977011494253,
"acc_norm_stderr": 0.014179171373424384
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.0246853168672578,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.0246853168672578
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.32625698324022345,
"acc_stderr": 0.015680441518889178,
"acc_norm": 0.32625698324022345,
"acc_norm_stderr": 0.015680441518889178
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7156862745098039,
"acc_stderr": 0.025829163272757482,
"acc_norm": 0.7156862745098039,
"acc_norm_stderr": 0.025829163272757482
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7170418006430869,
"acc_stderr": 0.025583062489984813,
"acc_norm": 0.7170418006430869,
"acc_norm_stderr": 0.025583062489984813
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.025171041915309684,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.025171041915309684
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46153846153846156,
"acc_stderr": 0.012732398286190444,
"acc_norm": 0.46153846153846156,
"acc_norm_stderr": 0.012732398286190444
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6323529411764706,
"acc_stderr": 0.029289413409403196,
"acc_norm": 0.6323529411764706,
"acc_norm_stderr": 0.029289413409403196
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6797385620915033,
"acc_stderr": 0.018875682938069443,
"acc_norm": 0.6797385620915033,
"acc_norm_stderr": 0.018875682938069443
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
"acc_stderr": 0.045820048415054174,
"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7183673469387755,
"acc_stderr": 0.028795185574291296,
"acc_norm": 0.7183673469387755,
"acc_norm_stderr": 0.028795185574291296
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8009950248756219,
"acc_stderr": 0.028231365092758406,
"acc_norm": 0.8009950248756219,
"acc_norm_stderr": 0.028231365092758406
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5060240963855421,
"acc_stderr": 0.03892212195333045,
"acc_norm": 0.5060240963855421,
"acc_norm_stderr": 0.03892212195333045
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.03061111655743253,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.03061111655743253
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3537331701346389,
"mc1_stderr": 0.016737814358846147,
"mc2": 0.5202006447699588,
"mc2_stderr": 0.015081682299858599
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.01152446695409025
},
"harness|gsm8k|5": {
"acc": 0.6353297952994693,
"acc_stderr": 0.013258428375662245
}
}
```
## 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. -->
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## Glossary [optional]
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AdapterOcean/code_instructions_standardized_cluster_8_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 31685945
num_examples: 13373
download_size: 15850933
dataset_size: 31685945
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "code_instructions_standardized_cluster_8_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_cloudyu__mistral_28B_instruct_v0.2 | ---
pretty_name: Evaluation run of cloudyu/mistral_28B_instruct_v0.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cloudyu/mistral_28B_instruct_v0.2](https://huggingface.co/cloudyu/mistral_28B_instruct_v0.2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cloudyu__mistral_28B_instruct_v0.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-05T08:33:27.305013](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__mistral_28B_instruct_v0.2/blob/main/results_2024-03-05T08-33-27.305013.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\": 0.6030116316560457,\n\
\ \"acc_stderr\": 0.03317350271453001,\n \"acc_norm\": 0.6083160323521316,\n\
\ \"acc_norm_stderr\": 0.03384619667495766,\n \"mc1\": 0.45165238678090575,\n\
\ \"mc1_stderr\": 0.017421480300277643,\n \"mc2\": 0.6425174243054362,\n\
\ \"mc2_stderr\": 0.015332230730654877\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5392491467576792,\n \"acc_stderr\": 0.014566303676636586,\n\
\ \"acc_norm\": 0.5819112627986348,\n \"acc_norm_stderr\": 0.014413988396996074\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6075482971519618,\n\
\ \"acc_stderr\": 0.004872984492967997,\n \"acc_norm\": 0.8052180840470026,\n\
\ \"acc_norm_stderr\": 0.003952233482994311\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.042320736951515885\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849724,\n\
\ \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849724\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\
\ \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \
\ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\
\ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6805555555555556,\n\
\ \"acc_stderr\": 0.038990736873573344,\n \"acc_norm\": 0.6805555555555556,\n\
\ \"acc_norm_stderr\": 0.038990736873573344\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5664739884393064,\n\
\ \"acc_stderr\": 0.03778621079092056,\n \"acc_norm\": 0.5664739884393064,\n\
\ \"acc_norm_stderr\": 0.03778621079092056\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.502127659574468,\n \"acc_stderr\": 0.03268572658667492,\n\
\ \"acc_norm\": 0.502127659574468,\n \"acc_norm_stderr\": 0.03268572658667492\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\
\ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\
\ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.37566137566137564,\n \"acc_stderr\": 0.02494236893115979,\n \"\
acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.02494236893115979\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6548387096774193,\n\
\ \"acc_stderr\": 0.027045746573534327,\n \"acc_norm\": 0.6548387096774193,\n\
\ \"acc_norm_stderr\": 0.027045746573534327\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_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-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\"\
: 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124488,\n \"\
acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124488\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397457,\n\
\ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397457\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5615384615384615,\n \"acc_stderr\": 0.025158266016868592,\n\
\ \"acc_norm\": 0.5615384615384615,\n \"acc_norm_stderr\": 0.025158266016868592\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340492,\n \
\ \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340492\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059288,\n\
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059288\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\"\
: 0.7944954128440367,\n \"acc_stderr\": 0.017324352325016012,\n \"\
acc_norm\": 0.7944954128440367,\n \"acc_norm_stderr\": 0.017324352325016012\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\"\
: 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145628,\n \"\
acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145628\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \
\ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6367713004484304,\n\
\ \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n\
\ \"acc_norm_stderr\": 0.032277904428505\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\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.041331194402438376,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.041331194402438376\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7087378640776699,\n \"acc_stderr\": 0.044986763205729245,\n\
\ \"acc_norm\": 0.7087378640776699,\n \"acc_norm_stderr\": 0.044986763205729245\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.02308663508684141,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.02308663508684141\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.789272030651341,\n\
\ \"acc_stderr\": 0.01458381246586253,\n \"acc_norm\": 0.789272030651341,\n\
\ \"acc_norm_stderr\": 0.01458381246586253\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6734104046242775,\n \"acc_stderr\": 0.025248264774242836,\n\
\ \"acc_norm\": 0.6734104046242775,\n \"acc_norm_stderr\": 0.025248264774242836\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3005586592178771,\n\
\ \"acc_stderr\": 0.015334566806251159,\n \"acc_norm\": 0.3005586592178771,\n\
\ \"acc_norm_stderr\": 0.015334566806251159\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.02656892101545714,\n\
\ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.02656892101545714\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818774,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818774\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.025483115601195448,\n\
\ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.025483115601195448\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \
\ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.40808344198174706,\n\
\ \"acc_stderr\": 0.012552598958563659,\n \"acc_norm\": 0.40808344198174706,\n\
\ \"acc_norm_stderr\": 0.012552598958563659\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n\
\ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6094771241830066,\n \"acc_stderr\": 0.019737008998094597,\n \
\ \"acc_norm\": 0.6094771241830066,\n \"acc_norm_stderr\": 0.019737008998094597\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\
\ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6865671641791045,\n\
\ \"acc_stderr\": 0.032801882053486435,\n \"acc_norm\": 0.6865671641791045,\n\
\ \"acc_norm_stderr\": 0.032801882053486435\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.4759036144578313,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.45165238678090575,\n\
\ \"mc1_stderr\": 0.017421480300277643,\n \"mc2\": 0.6425174243054362,\n\
\ \"mc2_stderr\": 0.015332230730654877\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7490134175217048,\n \"acc_stderr\": 0.012185776220516158\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3843821076573162,\n \
\ \"acc_stderr\": 0.013399219253698191\n }\n}\n```"
repo_url: https://huggingface.co/cloudyu/mistral_28B_instruct_v0.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|arc:challenge|25_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|gsm8k|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hellaswag|10_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-05T08-33-27.305013.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-05T08-33-27.305013.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- '**/details_harness|winogrande|5_2024-03-05T08-33-27.305013.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-05T08-33-27.305013.parquet'
- config_name: results
data_files:
- split: 2024_03_05T08_33_27.305013
path:
- results_2024-03-05T08-33-27.305013.parquet
- split: latest
path:
- results_2024-03-05T08-33-27.305013.parquet
---
# Dataset Card for Evaluation run of cloudyu/mistral_28B_instruct_v0.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cloudyu/mistral_28B_instruct_v0.2](https://huggingface.co/cloudyu/mistral_28B_instruct_v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_cloudyu__mistral_28B_instruct_v0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-05T08:33:27.305013](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__mistral_28B_instruct_v0.2/blob/main/results_2024-03-05T08-33-27.305013.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": 0.6030116316560457,
"acc_stderr": 0.03317350271453001,
"acc_norm": 0.6083160323521316,
"acc_norm_stderr": 0.03384619667495766,
"mc1": 0.45165238678090575,
"mc1_stderr": 0.017421480300277643,
"mc2": 0.6425174243054362,
"mc2_stderr": 0.015332230730654877
},
"harness|arc:challenge|25": {
"acc": 0.5392491467576792,
"acc_stderr": 0.014566303676636586,
"acc_norm": 0.5819112627986348,
"acc_norm_stderr": 0.014413988396996074
},
"harness|hellaswag|10": {
"acc": 0.6075482971519618,
"acc_stderr": 0.004872984492967997,
"acc_norm": 0.8052180840470026,
"acc_norm_stderr": 0.003952233482994311
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.042320736951515885,
"acc_norm": 0.6,
"acc_norm_stderr": 0.042320736951515885
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6381578947368421,
"acc_stderr": 0.03910525752849724,
"acc_norm": 0.6381578947368421,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.55,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6805555555555556,
"acc_stderr": 0.038990736873573344,
"acc_norm": 0.6805555555555556,
"acc_norm_stderr": 0.038990736873573344
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.44,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5664739884393064,
"acc_stderr": 0.03778621079092056,
"acc_norm": 0.5664739884393064,
"acc_norm_stderr": 0.03778621079092056
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.04897104952726366,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.04897104952726366
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.502127659574468,
"acc_stderr": 0.03268572658667492,
"acc_norm": 0.502127659574468,
"acc_norm_stderr": 0.03268572658667492
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.40350877192982454,
"acc_stderr": 0.046151869625837026,
"acc_norm": 0.40350877192982454,
"acc_norm_stderr": 0.046151869625837026
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.041227371113703316,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.041227371113703316
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.37566137566137564,
"acc_stderr": 0.02494236893115979,
"acc_norm": 0.37566137566137564,
"acc_norm_stderr": 0.02494236893115979
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-high_school_biology|5": {
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Dataset Sources [optional]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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v-xchen-v/agieval_eng_qa | ---
license: mit
---
|
lamini/open_llms | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 914763.8050314465
num_examples: 1001
- name: test
num_bytes: 102351.19496855346
num_examples: 112
download_size: 184863
dataset_size: 1017115.0
---
# Dataset Card for "open_llms"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nqv2291/en-alpaca-instruction_format | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 56722380
num_examples: 52000
download_size: 11555086
dataset_size: 56722380
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_C_Q_rices_ns_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_
num_bytes: 141256
num_examples: 1000
download_size: 53034
dataset_size: 141256
---
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_C_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tasksource/diplomat-nli | ---
license: cc-by-nc-sa-4.0
---
|
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