datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
higgsfield/dsml_original_loc | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 243684731
num_examples: 32477
download_size: 27760890
dataset_size: 243684731
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dsml_original_loc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mekaneeky/salt-llama-lgg-to-eng | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: ID
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 4130369
num_examples: 23947
- name: dev
num_bytes: 85575
num_examples: 500
- name: test
num_bytes: 87440
num_examples: 500
download_size: 2324474
dataset_size: 4303384
---
# Dataset Card for "salt-llama-lgg-to-eng"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nan-Do/instructional_code-search-net-go | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 122612124
num_examples: 203128
download_size: 45476654
dataset_size: 122612124
---
# Dataset Card for "instructional_code-search-net-go"
IT STILL REQUIRES MORE WORK.
PLEASE DON'T USE IT |
AdapterOcean/data-standardized_cluster_22_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 26759579
num_examples: 12736
download_size: 11363431
dataset_size: 26759579
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_22_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_deepseek-ai__deepseek-math-7b-instruct | ---
pretty_name: Evaluation run of deepseek-ai/deepseek-math-7b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [deepseek-ai/deepseek-math-7b-instruct](https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct)\
\ 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_deepseek-ai__deepseek-math-7b-instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-13T18:13:18.094811](https://huggingface.co/datasets/open-llm-leaderboard/details_deepseek-ai__deepseek-math-7b-instruct/blob/main/results_2024-03-13T18-13-18.094811.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\": 0.554019303836701,\n\
\ \"acc_stderr\": 0.034497761386808885,\n \"acc_norm\": 0.5618809813979222,\n\
\ \"acc_norm_stderr\": 0.035247964085412954,\n \"mc1\": 0.29253365973072215,\n\
\ \"mc1_stderr\": 0.015925597445286165,\n \"mc2\": 0.40156731347428204,\n\
\ \"mc2_stderr\": 0.014934119039002425\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5008532423208191,\n \"acc_stderr\": 0.014611369529813269,\n\
\ \"acc_norm\": 0.5341296928327645,\n \"acc_norm_stderr\": 0.014577311315231108\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5409281019717188,\n\
\ \"acc_stderr\": 0.004973036453863722,\n \"acc_norm\": 0.7149970125473013,\n\
\ \"acc_norm_stderr\": 0.004504932999736403\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411021,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411021\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4222222222222222,\n\
\ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.4222222222222222,\n\
\ \"acc_norm_stderr\": 0.04266763404099582\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\": 0.56,\n\
\ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5358490566037736,\n \"acc_stderr\": 0.030693675018458003,\n\
\ \"acc_norm\": 0.5358490566037736,\n \"acc_norm_stderr\": 0.030693675018458003\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5972222222222222,\n\
\ \"acc_stderr\": 0.04101405519842425,\n \"acc_norm\": 0.5972222222222222,\n\
\ \"acc_norm_stderr\": 0.04101405519842425\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\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\
: 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.5375722543352601,\n\
\ \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.5375722543352601,\n\
\ \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6425531914893617,\n \"acc_stderr\": 0.031329417894764254,\n\
\ \"acc_norm\": 0.6425531914893617,\n \"acc_norm_stderr\": 0.031329417894764254\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\": 0.6413793103448275,\n \"acc_stderr\": 0.03996629574876718,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.03996629574876718\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.5661375661375662,\n \"acc_stderr\": 0.025525034382474894,\n \"\
acc_norm\": 0.5661375661375662,\n \"acc_norm_stderr\": 0.025525034382474894\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542126,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542126\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6451612903225806,\n\
\ \"acc_stderr\": 0.027218889773308757,\n \"acc_norm\": 0.6451612903225806,\n\
\ \"acc_norm_stderr\": 0.027218889773308757\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6787878787878788,\n \"acc_stderr\": 0.036462049632538115,\n\
\ \"acc_norm\": 0.6787878787878788,\n \"acc_norm_stderr\": 0.036462049632538115\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6868686868686869,\n \"acc_stderr\": 0.033042050878136525,\n \"\
acc_norm\": 0.6868686868686869,\n \"acc_norm_stderr\": 0.033042050878136525\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6787564766839378,\n \"acc_stderr\": 0.033699508685490674,\n\
\ \"acc_norm\": 0.6787564766839378,\n \"acc_norm_stderr\": 0.033699508685490674\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5743589743589743,\n \"acc_stderr\": 0.025069094387296535,\n\
\ \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.025069094387296535\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.40370370370370373,\n \"acc_stderr\": 0.029914812342227624,\n \
\ \"acc_norm\": 0.40370370370370373,\n \"acc_norm_stderr\": 0.029914812342227624\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977927,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977927\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4105960264900662,\n \"acc_stderr\": 0.04016689594849927,\n \"\
acc_norm\": 0.4105960264900662,\n \"acc_norm_stderr\": 0.04016689594849927\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7376146788990826,\n \"acc_stderr\": 0.018861885021534727,\n \"\
acc_norm\": 0.7376146788990826,\n \"acc_norm_stderr\": 0.018861885021534727\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"\
acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5490196078431373,\n \"acc_stderr\": 0.03492406104163613,\n \"\
acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.03492406104163613\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6708860759493671,\n \"acc_stderr\": 0.030587326294702354,\n \
\ \"acc_norm\": 0.6708860759493671,\n \"acc_norm_stderr\": 0.030587326294702354\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5426008968609866,\n\
\ \"acc_stderr\": 0.03343577705583065,\n \"acc_norm\": 0.5426008968609866,\n\
\ \"acc_norm_stderr\": 0.03343577705583065\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\
\ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6611570247933884,\n \"acc_stderr\": 0.043207678075366705,\n \"\
acc_norm\": 0.6611570247933884,\n \"acc_norm_stderr\": 0.043207678075366705\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6388888888888888,\n\
\ \"acc_stderr\": 0.04643454608906276,\n \"acc_norm\": 0.6388888888888888,\n\
\ \"acc_norm_stderr\": 0.04643454608906276\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\
\ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\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\": 0.6893203883495146,\n \"acc_stderr\": 0.0458212416016155,\n\
\ \"acc_norm\": 0.6893203883495146,\n \"acc_norm_stderr\": 0.0458212416016155\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8034188034188035,\n\
\ \"acc_stderr\": 0.02603538609895129,\n \"acc_norm\": 0.8034188034188035,\n\
\ \"acc_norm_stderr\": 0.02603538609895129\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6730523627075351,\n\
\ \"acc_stderr\": 0.016774908180131463,\n \"acc_norm\": 0.6730523627075351,\n\
\ \"acc_norm_stderr\": 0.016774908180131463\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5664739884393064,\n \"acc_stderr\": 0.02668013476167922,\n\
\ \"acc_norm\": 0.5664739884393064,\n \"acc_norm_stderr\": 0.02668013476167922\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.29497206703910617,\n\
\ \"acc_stderr\": 0.015251931579208199,\n \"acc_norm\": 0.29497206703910617,\n\
\ \"acc_norm_stderr\": 0.015251931579208199\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.028580341065138296,\n\
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.028580341065138296\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5819935691318328,\n\
\ \"acc_stderr\": 0.028013651891995076,\n \"acc_norm\": 0.5819935691318328,\n\
\ \"acc_norm_stderr\": 0.028013651891995076\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.027801656212323667,\n\
\ \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.027801656212323667\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.37943262411347517,\n \"acc_stderr\": 0.0289473388516141,\n \
\ \"acc_norm\": 0.37943262411347517,\n \"acc_norm_stderr\": 0.0289473388516141\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.38461538461538464,\n\
\ \"acc_stderr\": 0.01242554841630294,\n \"acc_norm\": 0.38461538461538464,\n\
\ \"acc_norm_stderr\": 0.01242554841630294\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.41544117647058826,\n \"acc_stderr\": 0.029935342707877746,\n\
\ \"acc_norm\": 0.41544117647058826,\n \"acc_norm_stderr\": 0.029935342707877746\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5081699346405228,\n \"acc_stderr\": 0.02022513434305727,\n \
\ \"acc_norm\": 0.5081699346405228,\n \"acc_norm_stderr\": 0.02022513434305727\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n\
\ \"acc_stderr\": 0.04724577405731571,\n \"acc_norm\": 0.5818181818181818,\n\
\ \"acc_norm_stderr\": 0.04724577405731571\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.03063565515038764,\n\
\ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.03063565515038764\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7313432835820896,\n\
\ \"acc_stderr\": 0.03134328358208954,\n \"acc_norm\": 0.7313432835820896,\n\
\ \"acc_norm_stderr\": 0.03134328358208954\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.40963855421686746,\n\
\ \"acc_stderr\": 0.03828401115079021,\n \"acc_norm\": 0.40963855421686746,\n\
\ \"acc_norm_stderr\": 0.03828401115079021\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.03786720706234214,\n\
\ \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.03786720706234214\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29253365973072215,\n\
\ \"mc1_stderr\": 0.015925597445286165,\n \"mc2\": 0.40156731347428204,\n\
\ \"mc2_stderr\": 0.014934119039002425\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6574585635359116,\n \"acc_stderr\": 0.013337483579075923\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.19408642911296436,\n \
\ \"acc_stderr\": 0.010893918308192413\n }\n}\n```"
repo_url: https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct
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_12T07_42_48.023389
path:
- '**/details_harness|arc:challenge|25_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|arc:challenge|25_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|gsm8k|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|gsm8k|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hellaswag|10_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hellaswag|10_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T07-42-48.023389.parquet'
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- '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T07-42-48.023389.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T07-42-48.023389.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T07-42-48.023389.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-12T07-42-48.023389.parquet'
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- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T07-42-48.023389.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T07-42-48.023389.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T07-42-48.023389.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-12T07-42-48.023389.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-college_biology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-13T18-13-18.094811.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-13T18-13-18.094811.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-13T18-13-18.094811.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- '**/details_harness|winogrande|5_2024-03-12T07-42-48.023389.parquet'
- split: 2024_03_13T18_13_18.094811
path:
- '**/details_harness|winogrande|5_2024-03-13T18-13-18.094811.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-13T18-13-18.094811.parquet'
- config_name: results
data_files:
- split: 2024_03_12T07_42_48.023389
path:
- results_2024-03-12T07-42-48.023389.parquet
- split: 2024_03_13T18_13_18.094811
path:
- results_2024-03-13T18-13-18.094811.parquet
- split: latest
path:
- results_2024-03-13T18-13-18.094811.parquet
---
# Dataset Card for Evaluation run of deepseek-ai/deepseek-math-7b-instruct
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [deepseek-ai/deepseek-math-7b-instruct](https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct) 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_deepseek-ai__deepseek-math-7b-instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-13T18:13:18.094811](https://huggingface.co/datasets/open-llm-leaderboard/details_deepseek-ai__deepseek-math-7b-instruct/blob/main/results_2024-03-13T18-13-18.094811.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": 0.554019303836701,
"acc_stderr": 0.034497761386808885,
"acc_norm": 0.5618809813979222,
"acc_norm_stderr": 0.035247964085412954,
"mc1": 0.29253365973072215,
"mc1_stderr": 0.015925597445286165,
"mc2": 0.40156731347428204,
"mc2_stderr": 0.014934119039002425
},
"harness|arc:challenge|25": {
"acc": 0.5008532423208191,
"acc_stderr": 0.014611369529813269,
"acc_norm": 0.5341296928327645,
"acc_norm_stderr": 0.014577311315231108
},
"harness|hellaswag|10": {
"acc": 0.5409281019717188,
"acc_stderr": 0.004973036453863722,
"acc_norm": 0.7149970125473013,
"acc_norm_stderr": 0.004504932999736403
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411021,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411021
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4222222222222222,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.4222222222222222,
"acc_norm_stderr": 0.04266763404099582
},
"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": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5358490566037736,
"acc_stderr": 0.030693675018458003,
"acc_norm": 0.5358490566037736,
"acc_norm_stderr": 0.030693675018458003
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5972222222222222,
"acc_stderr": 0.04101405519842425,
"acc_norm": 0.5972222222222222,
"acc_norm_stderr": 0.04101405519842425
},
"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": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5375722543352601,
"acc_stderr": 0.0380168510452446,
"acc_norm": 0.5375722543352601,
"acc_norm_stderr": 0.0380168510452446
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6425531914893617,
"acc_stderr": 0.031329417894764254,
"acc_norm": 0.6425531914893617,
"acc_norm_stderr": 0.031329417894764254
},
"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": 0.6413793103448275,
"acc_stderr": 0.03996629574876718,
"acc_norm": 0.6413793103448275,
"acc_norm_stderr": 0.03996629574876718
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.5661375661375662,
"acc_stderr": 0.025525034382474894,
"acc_norm": 0.5661375661375662,
"acc_norm_stderr": 0.025525034382474894
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5,
"acc_stderr": 0.04472135954999579,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04472135954999579
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542126,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542126
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6451612903225806,
"acc_stderr": 0.027218889773308757,
"acc_norm": 0.6451612903225806,
"acc_norm_stderr": 0.027218889773308757
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.541871921182266,
"acc_stderr": 0.03505630140785741,
"acc_norm": 0.541871921182266,
"acc_norm_stderr": 0.03505630140785741
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6787878787878788,
"acc_stderr": 0.036462049632538115,
"acc_norm": 0.6787878787878788,
"acc_norm_stderr": 0.036462049632538115
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6868686868686869,
"acc_stderr": 0.033042050878136525,
"acc_norm": 0.6868686868686869,
"acc_norm_stderr": 0.033042050878136525
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6787564766839378,
"acc_stderr": 0.033699508685490674,
"acc_norm": 0.6787564766839378,
"acc_norm_stderr": 0.033699508685490674
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5743589743589743,
"acc_stderr": 0.025069094387296535,
"acc_norm": 0.5743589743589743,
"acc_norm_stderr": 0.025069094387296535
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.40370370370370373,
"acc_stderr": 0.029914812342227624,
"acc_norm": 0.40370370370370373,
"acc_norm_stderr": 0.029914812342227624
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
"acc_stderr": 0.030066761582977927,
"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.030066761582977927
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4105960264900662,
"acc_stderr": 0.04016689594849927,
"acc_norm": 0.4105960264900662,
"acc_norm_stderr": 0.04016689594849927
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7376146788990826,
"acc_stderr": 0.018861885021534727,
"acc_norm": 0.7376146788990826,
"acc_norm_stderr": 0.018861885021534727
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5787037037037037,
"acc_stderr": 0.03367462138896078,
"acc_norm": 0.5787037037037037,
"acc_norm_stderr": 0.03367462138896078
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5490196078431373,
"acc_stderr": 0.03492406104163613,
"acc_norm": 0.5490196078431373,
"acc_norm_stderr": 0.03492406104163613
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6708860759493671,
"acc_stderr": 0.030587326294702354,
"acc_norm": 0.6708860759493671,
"acc_norm_stderr": 0.030587326294702354
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5426008968609866,
"acc_stderr": 0.03343577705583065,
"acc_norm": 0.5426008968609866,
"acc_norm_stderr": 0.03343577705583065
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5954198473282443,
"acc_stderr": 0.043046937953806645,
"acc_norm": 0.5954198473282443,
"acc_norm_stderr": 0.043046937953806645
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6611570247933884,
"acc_stderr": 0.043207678075366705,
"acc_norm": 0.6611570247933884,
"acc_norm_stderr": 0.043207678075366705
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.04643454608906276,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.04643454608906276
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6687116564417178,
"acc_stderr": 0.03697983910025588,
"acc_norm": 0.6687116564417178,
"acc_norm_stderr": 0.03697983910025588
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.49107142857142855,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.49107142857142855,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.6893203883495146,
"acc_stderr": 0.0458212416016155,
"acc_norm": 0.6893203883495146,
"acc_norm_stderr": 0.0458212416016155
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8034188034188035,
"acc_stderr": 0.02603538609895129,
"acc_norm": 0.8034188034188035,
"acc_norm_stderr": 0.02603538609895129
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6730523627075351,
"acc_stderr": 0.016774908180131463,
"acc_norm": 0.6730523627075351,
"acc_norm_stderr": 0.016774908180131463
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5664739884393064,
"acc_stderr": 0.02668013476167922,
"acc_norm": 0.5664739884393064,
"acc_norm_stderr": 0.02668013476167922
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.29497206703910617,
"acc_stderr": 0.015251931579208199,
"acc_norm": 0.29497206703910617,
"acc_norm_stderr": 0.015251931579208199
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5294117647058824,
"acc_stderr": 0.028580341065138296,
"acc_norm": 0.5294117647058824,
"acc_norm_stderr": 0.028580341065138296
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5819935691318328,
"acc_stderr": 0.028013651891995076,
"acc_norm": 0.5819935691318328,
"acc_norm_stderr": 0.028013651891995076
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.027801656212323667,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.027801656212323667
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.37943262411347517,
"acc_stderr": 0.0289473388516141,
"acc_norm": 0.37943262411347517,
"acc_norm_stderr": 0.0289473388516141
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.38461538461538464,
"acc_stderr": 0.01242554841630294,
"acc_norm": 0.38461538461538464,
"acc_norm_stderr": 0.01242554841630294
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.41544117647058826,
"acc_stderr": 0.029935342707877746,
"acc_norm": 0.41544117647058826,
"acc_norm_stderr": 0.029935342707877746
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5081699346405228,
"acc_stderr": 0.02022513434305727,
"acc_norm": 0.5081699346405228,
"acc_norm_stderr": 0.02022513434305727
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5818181818181818,
"acc_stderr": 0.04724577405731571,
"acc_norm": 0.5818181818181818,
"acc_norm_stderr": 0.04724577405731571
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6448979591836734,
"acc_stderr": 0.03063565515038764,
"acc_norm": 0.6448979591836734,
"acc_norm_stderr": 0.03063565515038764
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7313432835820896,
"acc_stderr": 0.03134328358208954,
"acc_norm": 0.7313432835820896,
"acc_norm_stderr": 0.03134328358208954
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-virology|5": {
"acc": 0.40963855421686746,
"acc_stderr": 0.03828401115079021,
"acc_norm": 0.40963855421686746,
"acc_norm_stderr": 0.03828401115079021
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5789473684210527,
"acc_stderr": 0.03786720706234214,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.03786720706234214
},
"harness|truthfulqa:mc|0": {
"mc1": 0.29253365973072215,
"mc1_stderr": 0.015925597445286165,
"mc2": 0.40156731347428204,
"mc2_stderr": 0.014934119039002425
},
"harness|winogrande|5": {
"acc": 0.6574585635359116,
"acc_stderr": 0.013337483579075923
},
"harness|gsm8k|5": {
"acc": 0.19408642911296436,
"acc_stderr": 0.010893918308192413
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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arazd/tulu_dolly | ---
license: openrail
---
|
paduraru2009/imdb-sample2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 40107731
num_examples: 30000
- name: validation
num_bytes: 39127084
num_examples: 30000
download_size: 50593468
dataset_size: 79234815
---
# Dataset Card for "imdb-sample2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-elementary_mathematics | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: fewshot_context_neg
dtype: string
splits:
- name: dev
num_bytes: 4869
num_examples: 5
- name: test
num_bytes: 1253265
num_examples: 378
download_size: 136289
dataset_size: 1258134
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-elementary_mathematics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/rapi_nikke | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of rapi/ラピ/拉毗/라피 (Nikke: Goddess of Victory)
This is the dataset of rapi/ラピ/拉毗/라피 (Nikke: Goddess of Victory), containing 381 images and their tags.
The core tags of this character are `long_hair, breasts, bangs, brown_hair, hat, beret, large_breasts, black_headwear, red_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 381 | 688.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rapi_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 381 | 344.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rapi_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 985 | 751.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rapi_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 381 | 586.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rapi_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 985 | 1.10 GiB | [Download](https://huggingface.co/datasets/CyberHarem/rapi_nikke/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/rapi_nikke',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, black_jacket, black_thighhighs, long_sleeves, looking_at_viewer, red_necktie, solo, thighs, medium_breasts, open_jacket, parted_lips, red_gloves, sitting, belt_pouch, black_leotard, black_shirt, brown_thighhighs, closed_mouth, cropped_jacket, feet_out_of_frame |
| 1 | 14 |  |  |  |  |  | 1girl, black_thighhighs, solo, black_jacket, long_sleeves, black_leotard, closed_mouth, holding_gun, looking_at_viewer, pouch, black_gloves, rifle, red_necktie, ammunition_belt, cropped_jacket, orange_eyes, thighs |
| 2 | 5 |  |  |  |  |  | 1girl, black_panties, black_thighhighs, long_sleeves, looking_at_viewer, solo, white_background, ass_focus, black_gloves, blush, from_behind, simple_background, thighs, black_jacket, looking_back, skindentation, from_below, hand_on_own_ass, thong, underboob |
| 3 | 17 |  |  |  |  |  | 1girl, looking_at_viewer, red_necktie, black_jacket, solo, upper_body, grey_shirt, open_mouth, orange_eyes, black_choker, blush, brown_eyes, blurry, gloves, open_jacket, simple_background, white_background, hair_between_eyes, long_sleeves, uniform |
| 4 | 16 |  |  |  |  |  | 1boy, blush, hetero, 1girl, mosaic_censoring, open_mouth, penis, solo_focus, looking_back, long_sleeves, black_jacket, black_panties, cum_on_ass, sex, thong, ejaculation, girl_on_top, cum_on_body, looking_at_viewer, straddling, thighhighs, thighs |
| 5 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, penis, solo_focus, fellatio, mosaic_censoring, erection, long_sleeves, looking_at_viewer, :>=, black_gloves, black_jacket, blush, pov, sitting |
| 6 | 5 |  |  |  |  |  | 1boy, 1girl, black_jacket, fellatio, from_side, hetero, uncensored, clothed_female_nude_male, erection, closed_eyes, huge_breasts, solo_focus, star_(symbol), black_thighhighs, grey_bodysuit, grey_gloves, grey_headwear, grey_leotard, grey_thighhighs, kissing_penis, sitting |
| 7 | 16 |  |  |  |  |  | 1girl, solo, thighs, competition_swimsuit, highleg_swimsuit, looking_at_viewer, outdoors, blue_sky, day, blush, closed_mouth, bare_shoulders, cloud, cowboy_shot, goggles_on_head, bare_arms, black_one-piece_swimsuit, covered_navel, wet, cleavage, hair_intakes, water, beach, ocean, black_choker, standing, very_long_hair, ass, collarbone, eyewear_on_head, looking_back |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_jacket | black_thighhighs | long_sleeves | looking_at_viewer | red_necktie | solo | thighs | medium_breasts | open_jacket | parted_lips | red_gloves | sitting | belt_pouch | black_leotard | black_shirt | brown_thighhighs | closed_mouth | cropped_jacket | feet_out_of_frame | holding_gun | pouch | black_gloves | rifle | ammunition_belt | orange_eyes | black_panties | white_background | ass_focus | blush | from_behind | simple_background | looking_back | skindentation | from_below | hand_on_own_ass | thong | underboob | upper_body | grey_shirt | open_mouth | black_choker | brown_eyes | blurry | gloves | hair_between_eyes | uniform | 1boy | hetero | mosaic_censoring | penis | solo_focus | cum_on_ass | sex | ejaculation | girl_on_top | cum_on_body | straddling | thighhighs | fellatio | erection | :>= | pov | from_side | uncensored | clothed_female_nude_male | closed_eyes | huge_breasts | star_(symbol) | grey_bodysuit | grey_gloves | grey_headwear | grey_leotard | grey_thighhighs | kissing_penis | competition_swimsuit | highleg_swimsuit | outdoors | blue_sky | day | bare_shoulders | cloud | cowboy_shot | goggles_on_head | bare_arms | black_one-piece_swimsuit | covered_navel | wet | cleavage | hair_intakes | water | beach | ocean | standing | very_long_hair | ass | collarbone | eyewear_on_head |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------------------|:---------------|:--------------------|:--------------|:-------|:---------|:-----------------|:--------------|:--------------|:-------------|:----------|:-------------|:----------------|:--------------|:-------------------|:---------------|:-----------------|:--------------------|:--------------|:--------|:---------------|:--------|:------------------|:--------------|:----------------|:-------------------|:------------|:--------|:--------------|:--------------------|:---------------|:----------------|:-------------|:------------------|:--------|:------------|:-------------|:-------------|:-------------|:---------------|:-------------|:---------|:---------|:--------------------|:----------|:-------|:---------|:-------------------|:--------|:-------------|:-------------|:------|:--------------|:--------------|:--------------|:-------------|:-------------|:-----------|:-----------|:------|:------|:------------|:-------------|:---------------------------|:--------------|:---------------|:----------------|:----------------|:--------------|:----------------|:---------------|:------------------|:----------------|:-----------------------|:-------------------|:-----------|:-----------|:------|:-----------------|:--------|:--------------|:------------------|:------------|:---------------------------|:----------------|:------|:-----------|:---------------|:--------|:--------|:--------|:-----------|:-----------------|:------|:-------------|:------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | X | | | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | | X | X | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 17 |  |  |  |  |  | X | X | | X | X | X | X | | | X | | | | | | | | | | | | | | | | X | | X | | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 16 |  |  |  |  |  | X | X | | X | X | | | X | | | | | | | | | | | | | | | | | | | X | | | X | | | X | | | | X | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | | X | X | | | | | | | | X | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | X | | | | | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 16 |  |  |  |  |  | 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 |
|
rr/DDR | ---
license: pddl
---
|
kalivoda/dataset_easy_ocr_v0.3.0_clean | ---
dataset_info:
features:
- name: id
dtype: string
- name: words
sequence: string
- name: bboxes
sequence:
sequence: float32
- name: image_path
dtype: string
- name: ner_tags
sequence:
class_label:
names:
'0': DIC
'1': IBAN
'2': ICO
'3': O
'4': account_number
'5': bank_code
'6': const_symbol
'7': contr_address
'8': contr_name
'9': due_date
'10': invoice_date
'11': invoice_number
'12': qr_code
'13': spec_symbol
'14': total_amount
'15': var_symbol
splits:
- name: train
num_bytes: 20705074
num_examples: 2523
- name: val
num_bytes: 2370943
num_examples: 280
download_size: 7037725
dataset_size: 23076017
---
# Dataset Card for "dataset_easy_ocr_v0.3.0_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BangumiBase/dorohedoro | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Dorohedoro
This is the image base of bangumi Dorohedoro, we detected 23 characters, 1018 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 140 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 49 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 35 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 19 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 107 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 114 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 43 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 77 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 52 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 45 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 29 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 18 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 22 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 57 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 16 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 23 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 37 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 8 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 19 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 12 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 10 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 63 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Thanmay/truthful_qa_multiple_choice-hi | ---
dataset_info:
features:
- name: question
dtype: string
- name: mc1_targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int32
- name: mc2_targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int32
- name: itv2 hi question
dtype: string
- name: itv2 hi mc1_targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int64
- name: itv2 hi mc2_targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int64
splits:
- name: validation
num_bytes: 2177577
num_examples: 817
download_size: 710790
dataset_size: 2177577
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
scikit-learn/credit-card-clients | ---
license: cc0-1.0
---
## Default of Credit Card Clients Dataset
The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients).
**Dataset Information**
This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.
**Content**
There are 25 variables:
- ID: ID of each client
- LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit
- SEX: Gender (1=male, 2=female)
- EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)
- MARRIAGE: Marital status (1=married, 2=single, 3=others)
- AGE: Age in years
- PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above)
- PAY_2: Repayment status in August, 2005 (scale same as above)
- PAY_3: Repayment status in July, 2005 (scale same as above)
- PAY_4: Repayment status in June, 2005 (scale same as above)
- PAY_5: Repayment status in May, 2005 (scale same as above)
- PAY_6: Repayment status in April, 2005 (scale same as above)
- BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)
- BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)
- BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)
- BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)
- BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)
- BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)
- PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)
- PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)
- PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)
- PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)
- PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)
- PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)
- default.payment.next.month: Default payment (1=yes, 0=no)
**Inspiration**
Some ideas for exploration:
How does the probability of default payment vary by categories of different demographic variables?
Which variables are the strongest predictors of default payment?
**Acknowledgements**
Any publications based on this dataset should acknowledge the following:
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
|
Falah/chapter7_1_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 3050
num_examples: 10
download_size: 3219
dataset_size: 3050
---
# Dataset Card for "chapter7_1_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LucasThil/miniwob_plusplus_T5_randomized_ref2 | ---
dataset_info:
features:
- name: history_episodes
dtype: string
- name: instruction
dtype: string
- name: html_snippets
dtype: string
- name: actions
dtype: string
- name: refs
dtype: int64
- name: keydown_texts
dtype: string
splits:
- name: train
num_bytes: 267456938
num_examples: 60321
download_size: 0
dataset_size: 267456938
---
# Dataset Card for "miniwob_plusplus_T5_randomized_ref2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
christykoh/imdb_zh | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
splits:
- name: train
num_bytes: 18760648
num_examples: 25000
- name: test
num_bytes: 18574771
num_examples: 25000
download_size: 23908717
dataset_size: 37335419
---
# Dataset Card for "imdb_zh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Anas989898/DPO-datascience | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: regected
dtype: string
- name: chosen
dtype: string
splits:
- name: train
num_bytes: 2507192
num_examples: 1096
- name: test
num_bytes: 751956
num_examples: 300
download_size: 1510394
dataset_size: 3259148
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
CyberNative/Code_Vulnerability_Security_DPO | ---
license: apache-2.0
tags:
- dpo
- cybersecurity
- programming
- code
- Python
pretty_name: Code Vulnerability and Security DPO Dataset
---
# Cybernative.ai Code Vulnerability and Security Dataset
## Dataset Description
The Cybernative.ai Code Vulnerability and Security Dataset is a dataset of synthetic Data Programming by Demonstration (DPO) pairs, focusing on the intricate relationship between secure and insecure code across a variety of programming languages. This dataset is meticulously crafted to serve as a pivotal resource for researchers, cybersecurity professionals, and AI developers who are keen on understanding, identifying, and mitigating vulnerabilities in code.
This dataset is generated using [LoneStriker/deepseek-coder-33b-instruct-4.0bpw-h6-exl2](https://huggingface.co/LoneStriker/deepseek-coder-33b-instruct-4.0bpw-h6-exl2)
### Languages Covered
The dataset spans an array of popular programming languages, including but not limited to:
- C++
- Python
- Java
- JavaScript
- C#
- PHP
- Ruby
- Swift
- Go
- Kotlin
- Fortran
Each entry in the dataset is generated through a sophisticated AI-driven process, ensuring a diverse and realistic range of code examples. This approach guarantees that the dataset is not only extensive but also mirrors real-world coding practices and scenarios.
### Dataset Structure
The dataset is organized into pairs of vulnerable and fixed code snippets, accompanied by a task description that serves as a question. This structure is designed to facilitate the development and evaluation of AI models capable of understanding and rectifying code vulnerabilities.
- **Vulnerable Code**: A code snippet that contains a specific vulnerability, written in a professional, realistic manner but intentionally insecure and inefficient.
- **Fixed Code**: A secure and optimized version of the vulnerable code, adhering to best practices and efficient methods.
- **Task Description**: A high-level instruction that applies to both the vulnerable and fixed code, providing context and serving as a question for model evaluation.
### Use Cases
The Cybernative.ai Code Vulnerability and Security Dataset is ideal for a variety of applications, including but not limited to:
- Training AI models to identify code vulnerabilities.
- Developing tools for automated code review and security auditing.
- Enhancing educational resources for teaching secure coding practices.
- Benchmarking the performance of code analysis and vulnerability detection algorithms.
### Accessing the Dataset
The dataset is hosted on the Hugging Face Datasets platform, allowing for easy access and integration into machine learning workflows. Users can download the dataset directly from the platform and leverage its extensive tooling and community support for dataset manipulation and model training.
### Contributing
Cybernative.ai encourages contributions to the dataset. Whether it's by submitting additional code pairs, suggesting improvements, or reporting issues, community involvement is pivotal in ensuring the dataset's quality and relevance.
### About Cybernative.ai
Cybernative.ai is an AI Social Network dedicated to fostering innovation and collaboration in the field of artificial intelligence. By providing resources like the Code Vulnerability and Security Dataset, Cybernative.ai aims to empower developers, researchers, and enthusiasts to tackle the challenges of cybersecurity and AI development together.
Join us in our mission to make the digital world more secure through the power of AI. Visit [Cybernative.ai](https://cybernative.ai) to explore more resources, connect with experts, and contribute to various AI and cybersecurity projects. |
fw1zr/rahul-gandhi-captions | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4491114.0
num_examples: 116
download_size: 4452636
dataset_size: 4491114.0
---
# Dataset Card for "rahul-gandhi-captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
carnival13/eng_sur_DA_tokenized_rt5 | ---
dataset_info:
features:
- name: pass_label
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 104310930
num_examples: 155590
download_size: 23898508
dataset_size: 104310930
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eng_sur_DA_tokenized_rt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-from-one-sec-cv12/chunk_203 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1219101468
num_examples: 237549
download_size: 1245791171
dataset_size: 1219101468
---
# Dataset Card for "chunk_203"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_julleong__illuni-llama-2-ko-7b-test | ---
pretty_name: Evaluation run of julleong/illuni-llama-2-ko-7b-test
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [julleong/illuni-llama-2-ko-7b-test](https://huggingface.co/julleong/illuni-llama-2-ko-7b-test)\
\ 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_julleong__illuni-llama-2-ko-7b-test\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-07T14:52:33.862107](https://huggingface.co/datasets/open-llm-leaderboard/details_julleong__illuni-llama-2-ko-7b-test/blob/main/results_2024-03-07T14-52-33.862107.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\": 0.29338568029421425,\n\
\ \"acc_stderr\": 0.032029488235203775,\n \"acc_norm\": 0.2955428748433477,\n\
\ \"acc_norm_stderr\": 0.03281437925902047,\n \"mc1\": 0.19951040391676866,\n\
\ \"mc1_stderr\": 0.013989929967559647,\n \"mc2\": 0.3329691460247487,\n\
\ \"mc2_stderr\": 0.014677158673168721\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.3916382252559727,\n \"acc_stderr\": 0.014264122124938213,\n\
\ \"acc_norm\": 0.43430034129692835,\n \"acc_norm_stderr\": 0.014484703048857357\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4887472615016929,\n\
\ \"acc_stderr\": 0.004988517597998613,\n \"acc_norm\": 0.6485759808803028,\n\
\ \"acc_norm_stderr\": 0.00476439398511103\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680814,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680814\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.37037037037037035,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.37037037037037035,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.03459777606810538,\n\
\ \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.03459777606810538\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.02761116340239972,\n\
\ \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.02761116340239972\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.03745554791462457,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.03745554791462457\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\"\
: 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n\
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816508,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816508\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2254335260115607,\n\
\ \"acc_stderr\": 0.03186209851641143,\n \"acc_norm\": 0.2254335260115607,\n\
\ \"acc_norm_stderr\": 0.03186209851641143\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.038739587141493496,\n\
\ \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.038739587141493496\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.3446808510638298,\n \"acc_stderr\": 0.03106898596312215,\n\
\ \"acc_norm\": 0.3446808510638298,\n \"acc_norm_stderr\": 0.03106898596312215\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.03999423879281335,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.03999423879281335\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n\
\ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2275132275132275,\n \"acc_stderr\": 0.021591269407823785,\n \"\
acc_norm\": 0.2275132275132275,\n \"acc_norm_stderr\": 0.021591269407823785\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1746031746031746,\n\
\ \"acc_stderr\": 0.0339549002085611,\n \"acc_norm\": 0.1746031746031746,\n\
\ \"acc_norm_stderr\": 0.0339549002085611\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3,\n\
\ \"acc_stderr\": 0.02606936229533514,\n \"acc_norm\": 0.3,\n \
\ \"acc_norm_stderr\": 0.02606936229533514\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.031447125816782426,\n\
\ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.031447125816782426\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\
: 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.3212121212121212,\n \"acc_stderr\": 0.03646204963253812,\n\
\ \"acc_norm\": 0.3212121212121212,\n \"acc_norm_stderr\": 0.03646204963253812\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.31313131313131315,\n \"acc_stderr\": 0.033042050878136525,\n \"\
acc_norm\": 0.31313131313131315,\n \"acc_norm_stderr\": 0.033042050878136525\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.29015544041450775,\n \"acc_stderr\": 0.03275264467791516,\n\
\ \"acc_norm\": 0.29015544041450775,\n \"acc_norm_stderr\": 0.03275264467791516\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.24615384615384617,\n \"acc_stderr\": 0.021840866990423084,\n\
\ \"acc_norm\": 0.24615384615384617,\n \"acc_norm_stderr\": 0.021840866990423084\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25555555555555554,\n \"acc_stderr\": 0.026593939101844082,\n \
\ \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.026593939101844082\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.2815126050420168,\n \"acc_stderr\": 0.02921354941437217,\n \
\ \"acc_norm\": 0.2815126050420168,\n \"acc_norm_stderr\": 0.02921354941437217\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.24503311258278146,\n \"acc_stderr\": 0.035118075718047245,\n \"\
acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.035118075718047245\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.3614678899082569,\n \"acc_stderr\": 0.02059808200993737,\n \"\
acc_norm\": 0.3614678899082569,\n \"acc_norm_stderr\": 0.02059808200993737\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.23148148148148148,\n \"acc_stderr\": 0.028765111718046934,\n \"\
acc_norm\": 0.23148148148148148,\n \"acc_norm_stderr\": 0.028765111718046934\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.24019607843137256,\n \"acc_stderr\": 0.02998373305591361,\n \"\
acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.02998373305591361\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.2911392405063291,\n \"acc_stderr\": 0.029571601065753367,\n \
\ \"acc_norm\": 0.2911392405063291,\n \"acc_norm_stderr\": 0.029571601065753367\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3721973094170404,\n\
\ \"acc_stderr\": 0.03244305283008731,\n \"acc_norm\": 0.3721973094170404,\n\
\ \"acc_norm_stderr\": 0.03244305283008731\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847835,\n\
\ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847835\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.38016528925619836,\n \"acc_stderr\": 0.04431324501968432,\n \"\
acc_norm\": 0.38016528925619836,\n \"acc_norm_stderr\": 0.04431324501968432\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3055555555555556,\n\
\ \"acc_stderr\": 0.04453197507374984,\n \"acc_norm\": 0.3055555555555556,\n\
\ \"acc_norm_stderr\": 0.04453197507374984\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.2883435582822086,\n \"acc_stderr\": 0.035590395316173425,\n\
\ \"acc_norm\": 0.2883435582822086,\n \"acc_norm_stderr\": 0.035590395316173425\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.042878587513404544,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.042878587513404544\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.33980582524271846,\n \"acc_stderr\": 0.046897659372781335,\n\
\ \"acc_norm\": 0.33980582524271846,\n \"acc_norm_stderr\": 0.046897659372781335\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.37606837606837606,\n\
\ \"acc_stderr\": 0.031733936329694803,\n \"acc_norm\": 0.37606837606837606,\n\
\ \"acc_norm_stderr\": 0.031733936329694803\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.3895274584929757,\n\
\ \"acc_stderr\": 0.0174380825562646,\n \"acc_norm\": 0.3895274584929757,\n\
\ \"acc_norm_stderr\": 0.0174380825562646\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.2861271676300578,\n \"acc_stderr\": 0.02433214677913413,\n\
\ \"acc_norm\": 0.2861271676300578,\n \"acc_norm_stderr\": 0.02433214677913413\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24134078212290502,\n\
\ \"acc_stderr\": 0.014310999547961436,\n \"acc_norm\": 0.24134078212290502,\n\
\ \"acc_norm_stderr\": 0.014310999547961436\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.025553169991826514,\n\
\ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.025553169991826514\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3086816720257235,\n\
\ \"acc_stderr\": 0.026236965881153262,\n \"acc_norm\": 0.3086816720257235,\n\
\ \"acc_norm_stderr\": 0.026236965881153262\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2839506172839506,\n \"acc_stderr\": 0.02508947852376513,\n\
\ \"acc_norm\": 0.2839506172839506,\n \"acc_norm_stderr\": 0.02508947852376513\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.28368794326241137,\n \"acc_stderr\": 0.02689170942834396,\n \
\ \"acc_norm\": 0.28368794326241137,\n \"acc_norm_stderr\": 0.02689170942834396\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2666232073011734,\n\
\ \"acc_stderr\": 0.011293836031612135,\n \"acc_norm\": 0.2666232073011734,\n\
\ \"acc_norm_stderr\": 0.011293836031612135\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3014705882352941,\n \"acc_stderr\": 0.027875982114273168,\n\
\ \"acc_norm\": 0.3014705882352941,\n \"acc_norm_stderr\": 0.027875982114273168\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.29248366013071897,\n \"acc_stderr\": 0.01840341571010979,\n \
\ \"acc_norm\": 0.29248366013071897,\n \"acc_norm_stderr\": 0.01840341571010979\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.32727272727272727,\n\
\ \"acc_stderr\": 0.04494290866252088,\n \"acc_norm\": 0.32727272727272727,\n\
\ \"acc_norm_stderr\": 0.04494290866252088\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.22857142857142856,\n \"acc_stderr\": 0.02688214492230774,\n\
\ \"acc_norm\": 0.22857142857142856,\n \"acc_norm_stderr\": 0.02688214492230774\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.29850746268656714,\n\
\ \"acc_stderr\": 0.03235743789355041,\n \"acc_norm\": 0.29850746268656714,\n\
\ \"acc_norm_stderr\": 0.03235743789355041\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3313253012048193,\n\
\ \"acc_stderr\": 0.03664314777288086,\n \"acc_norm\": 0.3313253012048193,\n\
\ \"acc_norm_stderr\": 0.03664314777288086\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.38596491228070173,\n \"acc_stderr\": 0.03733756969066165,\n\
\ \"acc_norm\": 0.38596491228070173,\n \"acc_norm_stderr\": 0.03733756969066165\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.19951040391676866,\n\
\ \"mc1_stderr\": 0.013989929967559647,\n \"mc2\": 0.3329691460247487,\n\
\ \"mc2_stderr\": 0.014677158673168721\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6377269139700079,\n \"acc_stderr\": 0.013508855476252508\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.024260803639120546,\n \
\ \"acc_stderr\": 0.004238007900001403\n }\n}\n```"
repo_url: https://huggingface.co/julleong/illuni-llama-2-ko-7b-test
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_07T14_52_33.862107
path:
- '**/details_harness|arc:challenge|25_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|gsm8k|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hellaswag|10_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T14-52-33.862107.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T14-52-33.862107.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- '**/details_harness|winogrande|5_2024-03-07T14-52-33.862107.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-07T14-52-33.862107.parquet'
- config_name: results
data_files:
- split: 2024_03_07T14_52_33.862107
path:
- results_2024-03-07T14-52-33.862107.parquet
- split: latest
path:
- results_2024-03-07T14-52-33.862107.parquet
---
# Dataset Card for Evaluation run of julleong/illuni-llama-2-ko-7b-test
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [julleong/illuni-llama-2-ko-7b-test](https://huggingface.co/julleong/illuni-llama-2-ko-7b-test) 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_julleong__illuni-llama-2-ko-7b-test",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-07T14:52:33.862107](https://huggingface.co/datasets/open-llm-leaderboard/details_julleong__illuni-llama-2-ko-7b-test/blob/main/results_2024-03-07T14-52-33.862107.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": {
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"acc_stderr": 0.032029488235203775,
"acc_norm": 0.2955428748433477,
"acc_norm_stderr": 0.03281437925902047,
"mc1": 0.19951040391676866,
"mc1_stderr": 0.013989929967559647,
"mc2": 0.3329691460247487,
"mc2_stderr": 0.014677158673168721
},
"harness|arc:challenge|25": {
"acc": 0.3916382252559727,
"acc_stderr": 0.014264122124938213,
"acc_norm": 0.43430034129692835,
"acc_norm_stderr": 0.014484703048857357
},
"harness|hellaswag|10": {
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"acc_stderr": 0.004988517597998613,
"acc_norm": 0.6485759808803028,
"acc_norm_stderr": 0.00476439398511103
},
"harness|hendrycksTest-abstract_algebra|5": {
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},
"harness|hendrycksTest-anatomy|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.024260803639120546,
"acc_stderr": 0.004238007900001403
}
}
```
## Dataset Details
### Dataset Description
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SiguienteGlobal/linguistica_assist | ---
license: apache-2.0
language:
- es
tags:
- code
pretty_name: linguistica_assist
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
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## Dataset Details
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[More Information Needed]
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[More Information Needed]
### Source Data
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[More Information Needed]
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Nexdata/Interspeech2020_Accented_English_Speech_Recognition_Competition_Data | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Interspeech2020_Accented_English_Speech_Recognition_Competition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/1169?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Interspeech2,020 Accented English Speech Recognition Competition Data. The text has been proofread manually with high accuracy; this data set can be used for automatic speech recognition, machine translation, and voiceprint recognition.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1169?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Accented English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
MoritzLaurer/dataset_test_concat_nli | ---
dataset_info:
features:
- name: text
dtype: string
- name: hypothesis
dtype: string
- name: labels
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: task_name
dtype: string
splits:
- name: train
num_bytes: 15114416
num_examples: 59140
download_size: 8715544
dataset_size: 15114416
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset_test_concat_nli"
Dataset for testing a universal classifier. Additional information and training code available here: https://github.com/MoritzLaurer/zeroshot-classifier
|
tohoku-nlp/multi-vidsum-eval | ---
license: apache-2.0
---
|
bigscience-data/roots_id_ted_talks_iwslt | ---
language: id
license: cc-by-nc-nd-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_id_ted_talks_iwslt
# WIT Ted Talks
- Dataset uid: `ted_talks_iwslt`
### Description
The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform.
### Homepage
https://github.com/huggingface/datasets/blob/master/datasets/ted_talks_iwslt/README.md
### Licensing
- open license
- cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
TED makes its collection of video recordings and transcripts of talks available under the Creative Commons BY-NC-ND license (look here). WIT3 acknowledges the authorship of TED talks (BY condition) and does not redistribute transcripts for commercial purposes (NC). As regards the integrity of the work (ND), WIT3 only changes the format of the container, while preserving the original contents. WIT3 aims to support research on human language processing as well as the diffusion of TED Talks!
### Speaker Locations
- Southern Europe
- Italy
### Sizes
- 0.0305 % of total
- 0.0736 % of ar
- 0.2002 % of pt
- 0.0128 % of zh
- 0.2236 % of vi
- 0.0330 % of fr
- 0.0545 % of es
- 0.0122 % of en
- 0.3704 % of id
- 0.0373 % of indic-hi
- 0.0330 % of indic-ta
- 0.1393 % of indic-mr
- 0.0305 % of ca
- 0.1179 % of indic-ur
- 0.0147 % of indic-bn
- 0.0240 % of indic-ml
- 0.0244 % of indic-te
- 0.0503 % of indic-gu
- 0.0211 % of indic-kn
- 0.0274 % of eu
- 0.0023 % of indic-as
- 0.0001 % of indic-pa
### BigScience processing steps
#### Filters applied to: ar
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: zh
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: ca
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ur
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-as
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-pa
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
|
open-llm-leaderboard/details_juhwanlee__experiment2-cause-v1 | ---
pretty_name: Evaluation run of juhwanlee/experiment2-cause-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [juhwanlee/experiment2-cause-v1](https://huggingface.co/juhwanlee/experiment2-cause-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_juhwanlee__experiment2-cause-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-05T05:00:39.006892](https://huggingface.co/datasets/open-llm-leaderboard/details_juhwanlee__experiment2-cause-v1/blob/main/results_2024-03-05T05-00-39.006892.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\": 0.6347236795836808,\n\
\ \"acc_stderr\": 0.03239027557700436,\n \"acc_norm\": 0.6403177585491361,\n\
\ \"acc_norm_stderr\": 0.033044435643731676,\n \"mc1\": 0.32313341493268055,\n\
\ \"mc1_stderr\": 0.016371836286454604,\n \"mc2\": 0.4719694152855096,\n\
\ \"mc2_stderr\": 0.014750153145318967\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5716723549488054,\n \"acc_stderr\": 0.014460496367599012,\n\
\ \"acc_norm\": 0.6100682593856656,\n \"acc_norm_stderr\": 0.014252959848892893\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6318462457677754,\n\
\ \"acc_stderr\": 0.004813177057496268,\n \"acc_norm\": 0.8337980481975702,\n\
\ \"acc_norm_stderr\": 0.003715010224478618\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.0387813988879761,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.0387813988879761\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\
\ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n\
\ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\
\ \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416906,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416906\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.38095238095238093,\n \"acc_stderr\": 0.025010749116137602,\n \"\
acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.025010749116137602\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7580645161290323,\n\
\ \"acc_stderr\": 0.02436259969303109,\n \"acc_norm\": 0.7580645161290323,\n\
\ \"acc_norm_stderr\": 0.02436259969303109\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\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\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6487179487179487,\n \"acc_stderr\": 0.024203665177902803,\n\
\ \"acc_norm\": 0.6487179487179487,\n \"acc_norm_stderr\": 0.024203665177902803\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35555555555555557,\n \"acc_stderr\": 0.02918571494985741,\n \
\ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.02918571494985741\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977924,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977924\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8238532110091743,\n \"acc_stderr\": 0.016332882393431388,\n \"\
acc_norm\": 0.8238532110091743,\n \"acc_norm_stderr\": 0.016332882393431388\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\
acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849316,\n \"\
acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849316\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7932489451476793,\n \"acc_stderr\": 0.0263616516683891,\n \
\ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.0263616516683891\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\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\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\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8186462324393359,\n\
\ \"acc_stderr\": 0.013778693778464074,\n \"acc_norm\": 0.8186462324393359,\n\
\ \"acc_norm_stderr\": 0.013778693778464074\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\
\ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3787709497206704,\n\
\ \"acc_stderr\": 0.01622353351036511,\n \"acc_norm\": 0.3787709497206704,\n\
\ \"acc_norm_stderr\": 0.01622353351036511\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\
\ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\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\": 0.44328552803129073,\n\
\ \"acc_stderr\": 0.01268781841959992,\n \"acc_norm\": 0.44328552803129073,\n\
\ \"acc_norm_stderr\": 0.01268781841959992\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6544117647058824,\n \"acc_stderr\": 0.028888193103988633,\n\
\ \"acc_norm\": 0.6544117647058824,\n \"acc_norm_stderr\": 0.028888193103988633\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6568627450980392,\n \"acc_stderr\": 0.019206606848825362,\n \
\ \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.019206606848825362\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.710204081632653,\n \"acc_stderr\": 0.029043088683304335,\n\
\ \"acc_norm\": 0.710204081632653,\n \"acc_norm_stderr\": 0.029043088683304335\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32313341493268055,\n\
\ \"mc1_stderr\": 0.016371836286454604,\n \"mc2\": 0.4719694152855096,\n\
\ \"mc2_stderr\": 0.014750153145318967\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7900552486187845,\n \"acc_stderr\": 0.01144628062926263\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.38968915845337376,\n \
\ \"acc_stderr\": 0.013433123236110702\n }\n}\n```"
repo_url: https://huggingface.co/juhwanlee/experiment2-cause-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_03_05T05_00_39.006892
path:
- '**/details_harness|arc:challenge|25_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|gsm8k|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hellaswag|10_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-05T05-00-39.006892.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-05T05-00-39.006892.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- '**/details_harness|winogrande|5_2024-03-05T05-00-39.006892.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-05T05-00-39.006892.parquet'
- config_name: results
data_files:
- split: 2024_03_05T05_00_39.006892
path:
- results_2024-03-05T05-00-39.006892.parquet
- split: latest
path:
- results_2024-03-05T05-00-39.006892.parquet
---
# Dataset Card for Evaluation run of juhwanlee/experiment2-cause-v1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [juhwanlee/experiment2-cause-v1](https://huggingface.co/juhwanlee/experiment2-cause-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_juhwanlee__experiment2-cause-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-05T05:00:39.006892](https://huggingface.co/datasets/open-llm-leaderboard/details_juhwanlee__experiment2-cause-v1/blob/main/results_2024-03-05T05-00-39.006892.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": 0.6347236795836808,
"acc_stderr": 0.03239027557700436,
"acc_norm": 0.6403177585491361,
"acc_norm_stderr": 0.033044435643731676,
"mc1": 0.32313341493268055,
"mc1_stderr": 0.016371836286454604,
"mc2": 0.4719694152855096,
"mc2_stderr": 0.014750153145318967
},
"harness|arc:challenge|25": {
"acc": 0.5716723549488054,
"acc_stderr": 0.014460496367599012,
"acc_norm": 0.6100682593856656,
"acc_norm_stderr": 0.014252959848892893
},
"harness|hellaswag|10": {
"acc": 0.6318462457677754,
"acc_stderr": 0.004813177057496268,
"acc_norm": 0.8337980481975702,
"acc_norm_stderr": 0.003715010224478618
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6513157894736842,
"acc_stderr": 0.0387813988879761,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.0387813988879761
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
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"acc": 0.38968915845337376,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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chenbobo/chat | ---
license: unlicense
task_chat:
- null
task_categories:
- text-generation
language:
- zh
tags:
- finance
pretty_name: tyin_demo
size_categories:
- n<1K
--- |
Pablao0948/Dark_Giovanni | ---
license: openrail
---
|
imoxto/prompt_injection_cleaned_dataset-v2 | ---
dataset_info:
features:
- name: model
dtype: string
- name: text
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 670958021
num_examples: 535105
download_size: 79246765
dataset_size: 670958021
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "prompt_injection_cleaned_dataset-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HamdanXI/paradetox-1Token-Split-MASK | ---
dataset_info:
features:
- name: labels
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 413629
num_examples: 3784
- name: validation
num_bytes: 88206
num_examples: 811
- name: test
num_bytes: 86840
num_examples: 811
download_size: 390168
dataset_size: 588675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
learn3r/summ_screen_fd_memsum_bp | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 7002624
num_examples: 3673
- name: validation
num_bytes: 676928
num_examples: 338
- name: test
num_bytes: 717198
num_examples: 337
download_size: 410312
dataset_size: 8396750
---
# Dataset Card for "summ_screen_fd_memsum_bp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Edsodre/mari2 | ---
license: openrail
---
|
Alexisnlxoekdk/MCkevindataset | ---
license: openrail
---
|
gvlassis/shakespearefirstfolio | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4077942
num_examples: 30
- name: validation
num_bytes: 245785
num_examples: 2
- name: test
num_bytes: 506679
num_examples: 4
download_size: 3073023
dataset_size: 4830406
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
task_categories:
- text-generation
language:
- en
tags:
- shakespeare
size_categories:
- n<1K
---
# shakespearefirstfolio
## About
🎭 Shakespeare's First Folio (a collection of 36 of Shakespeare's plays) as a Hugging Face dataset!
## Description
In 2015, Andrej Karpathy wrote a post called "The Unreasonable Effectiveness of Recurrent Neural Networks" in his blog. For the needs of this post, he created tinyshakespeare, a subset of Shakespeare's works in a single 40,000 lines file. Surprisingly, language models trained from scratch on this tiny dataset can produce samples that look very close to those written by Shakespeare himself.
Since then, tinyshakespeare has been the defacto dataset used as a first test while developing language models. Unfortunately, it has some problems:
1) It is a single file, which makes further processing difficult
2) It does not contain all of Shakespeare's works
3) It is not clear exactly what works and to what extend are included
This dataset tries to address these problems. It is ~4 times bigger than tinyshakespeare.
It was manually collected from [Folger Shakespeare Library](https://www.folger.edu/).
## Usage
import datasets
dataset = datasets.load_dataset("gvlassis/shakespearefirstfolio") |
open-llm-leaderboard/details_migtissera__Synthia-7B | ---
pretty_name: Evaluation run of migtissera/Synthia-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [migtissera/Synthia-7B](https://huggingface.co/migtissera/Synthia-7B) 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_migtissera__Synthia-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T06:07:54.738296](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-7B/blob/main/results_2023-10-15T06-07-54.738296.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\": 0.07151845637583892,\n\
\ \"em_stderr\": 0.00263897548039012,\n \"f1\": 0.14513737416107345,\n\
\ \"f1_stderr\": 0.0029452435334875074,\n \"acc\": 0.4043291747772373,\n\
\ \"acc_stderr\": 0.009561470405449964\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.07151845637583892,\n \"em_stderr\": 0.00263897548039012,\n\
\ \"f1\": 0.14513737416107345,\n \"f1_stderr\": 0.0029452435334875074\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06595905989385899,\n \
\ \"acc_stderr\": 0.006836951192034222\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7426992896606156,\n \"acc_stderr\": 0.012285989618865708\n\
\ }\n}\n```"
repo_url: https://huggingface.co/migtissera/Synthia-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: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|arc:challenge|25_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T06_07_54.738296
path:
- '**/details_harness|drop|3_2023-10-15T06-07-54.738296.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T06-07-54.738296.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T06_07_54.738296
path:
- '**/details_harness|gsm8k|5_2023-10-15T06-07-54.738296.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T06-07-54.738296.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hellaswag|10_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T17:21:07.158534.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T17:21:07.158534.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T17:21:07.158534.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T06_07_54.738296
path:
- '**/details_harness|winogrande|5_2023-10-15T06-07-54.738296.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T06-07-54.738296.parquet'
- config_name: results
data_files:
- split: 2023_08_17T17_21_07.158534
path:
- results_2023-08-17T17:21:07.158534.parquet
- split: 2023_10_15T06_07_54.738296
path:
- results_2023-10-15T06-07-54.738296.parquet
- split: latest
path:
- results_2023-10-15T06-07-54.738296.parquet
---
# Dataset Card for Evaluation run of migtissera/Synthia-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/migtissera/Synthia-7B
- **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 [migtissera/Synthia-7B](https://huggingface.co/migtissera/Synthia-7B) 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_migtissera__Synthia-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T06:07:54.738296](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-7B/blob/main/results_2023-10-15T06-07-54.738296.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": 0.07151845637583892,
"em_stderr": 0.00263897548039012,
"f1": 0.14513737416107345,
"f1_stderr": 0.0029452435334875074,
"acc": 0.4043291747772373,
"acc_stderr": 0.009561470405449964
},
"harness|drop|3": {
"em": 0.07151845637583892,
"em_stderr": 0.00263897548039012,
"f1": 0.14513737416107345,
"f1_stderr": 0.0029452435334875074
},
"harness|gsm8k|5": {
"acc": 0.06595905989385899,
"acc_stderr": 0.006836951192034222
},
"harness|winogrande|5": {
"acc": 0.7426992896606156,
"acc_stderr": 0.012285989618865708
}
}
```
### 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] |
yuvalkirstain/pexel_images | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 27590932.0
num_examples: 80
download_size: 27589857
dataset_size: 27590932.0
---
# Dataset Card for "pexel_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Afjalru/loan-prediction | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Guanaco-1k: Lazy Llama 2 Formatting
This is a subset (1000 samples) of the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing).
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
|
pradeep239/phil_Image_250Pdfs | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 444300577.0
num_examples: 867
- name: validation
num_bytes: 52961081.0
num_examples: 102
- name: test
num_bytes: 25115003.0
num_examples: 51
download_size: 434069431
dataset_size: 522376661.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
jasonzsxie/my_dataset | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 64049.0
num_examples: 1
download_size: 65151
dataset_size: 64049.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
pgwi/clean_fashion_data | ---
license: apache-2.0
---
|
language-plus-molecules/LPM-24_train-extra | ---
dataset_info:
features:
- name: molecule
dtype: string
- name: caption
dtype: string
splits:
- name: train
num_bytes: 276260470
num_examples: 802800
- name: split_train
num_bytes: 219611475
num_examples: 634320
- name: split_valid
num_bytes: 56648995
num_examples: 168480
download_size: 78056020
dataset_size: 552520940
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: split_train
path: data/split_train-*
- split: split_valid
path: data/split_valid-*
---
|
correll/semanticsegmentationandposeestimationfromrgbd | ---
license: mit
task_categories:
- image-segmentation
- image-classification
- object-detection
pretty_name: Semantic segmenation and pose estimation from RGB-D
dataset_info:
features:
- name: rgb
dtype: image
- name: depth
dtype: image
- name: mask
dtype: image
- name: meta
list:
- name: colors
sequence: float64
- name: file
dtype: string
- name: id
dtype: int64
- name: model
dtype: string
- name: numberOfColors
dtype: int64
- name: orientation
sequence: float64
- name: position
sequence: float64
- name: positionOnImage
sequence: int64
- name: size
sequence: float64
- name: sizeOnImage
sequence: int64
splits:
- name: train
num_bytes: 3340733260.96
num_examples: 1106
download_size: 3319212411
dataset_size: 3340733260.96
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
RGB-D dataset for instance segmentation (from RGB or depth) and pose estimation of individual objects. Data has been generated by randomizing bin contents in Webots.
Each instance contains a mask image as well meta data containing labels, position, and size of each object.
<video src='https://cdn-uploads.huggingface.co/production/uploads/655b1b359d249b4ab388d4a2/l6b76ezxkPi6lG3Fr6_kj.mp4' width=720/>
You can create your own data by opening webots_grasp.wbt in the world directory using [Webots](https://www.cyberbotics.com). |
Gabriel1898/poze1 | ---
license: openrail
---
|
huggingartists/aikko | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/aikko"
## 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:** 1.029888 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/a1a40316d1405fa83df2a21923d64168.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/aikko">
<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">aikko</div>
<a href="https://genius.com/artists/aikko">
<div style="text-align: center; font-size: 14px;">@aikko</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/aikko).
### 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/aikko")
```
## 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|
|------:|---------:|---:|
|305| -| -|
'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/aikko")
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)
|
ppxscal/citation-network-v1-jaccard | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: node1
dtype: int64
- name: abstract1
dtype: string
- name: node2
dtype: int64
- name: abstract2
dtype: string
- name: jaccard_score
dtype: float64
splits:
- name: train
num_bytes: 928245561
num_examples: 631592
download_size: 300134106
dataset_size: 928245561
---
# Dataset Card for "embeddings-network-jaccard"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
seongwoon/industry-occupation | ---
license: cc-by-nc-nd-4.0
---
|
open-llm-leaderboard/details_Severian__ANIMA-Nectar-v3 | ---
pretty_name: Evaluation run of Severian/ANIMA-Nectar-v3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Severian/ANIMA-Nectar-v3](https://huggingface.co/Severian/ANIMA-Nectar-v3) 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_Severian__ANIMA-Nectar-v3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-09T16:02:02.105784](https://huggingface.co/datasets/open-llm-leaderboard/details_Severian__ANIMA-Nectar-v3/blob/main/results_2023-12-09T16-02-02.105784.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\": 0.5279231991868109,\n\
\ \"acc_stderr\": 0.034095739528534785,\n \"acc_norm\": 0.5365465936132023,\n\
\ \"acc_norm_stderr\": 0.0349316183151297,\n \"mc1\": 0.3108935128518972,\n\
\ \"mc1_stderr\": 0.016203316673559693,\n \"mc2\": 0.4616473915095851,\n\
\ \"mc2_stderr\": 0.014431098139511664\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.454778156996587,\n \"acc_stderr\": 0.014551507060836353,\n\
\ \"acc_norm\": 0.4948805460750853,\n \"acc_norm_stderr\": 0.014610624890309154\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5621390161322446,\n\
\ \"acc_stderr\": 0.004951097802775953,\n \"acc_norm\": 0.7599083847839075,\n\
\ \"acc_norm_stderr\": 0.004262659388824526\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.4888888888888889,\n\
\ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\
\ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.506578947368421,\n \"acc_stderr\": 0.040685900502249704,\n\
\ \"acc_norm\": 0.506578947368421,\n \"acc_norm_stderr\": 0.040685900502249704\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\
\ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5962264150943396,\n \"acc_stderr\": 0.03019761160019795,\n\
\ \"acc_norm\": 0.5962264150943396,\n \"acc_norm_stderr\": 0.03019761160019795\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5416666666666666,\n\
\ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n\
\ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n\
\ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5433526011560693,\n\
\ \"acc_stderr\": 0.03798106566014498,\n \"acc_norm\": 0.5433526011560693,\n\
\ \"acc_norm_stderr\": 0.03798106566014498\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207763,\n\
\ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207763\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4723404255319149,\n \"acc_stderr\": 0.03263597118409769,\n\
\ \"acc_norm\": 0.4723404255319149,\n \"acc_norm_stderr\": 0.03263597118409769\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\
\ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\
\ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\
\ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3835978835978836,\n \"acc_stderr\": 0.025043757318520196,\n \"\
acc_norm\": 0.3835978835978836,\n \"acc_norm_stderr\": 0.025043757318520196\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3253968253968254,\n\
\ \"acc_stderr\": 0.041905964388711366,\n \"acc_norm\": 0.3253968253968254,\n\
\ \"acc_norm_stderr\": 0.041905964388711366\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.6258064516129033,\n\
\ \"acc_stderr\": 0.027528904299845704,\n \"acc_norm\": 0.6258064516129033,\n\
\ \"acc_norm_stderr\": 0.027528904299845704\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.43842364532019706,\n \"acc_stderr\": 0.03491207857486517,\n\
\ \"acc_norm\": 0.43842364532019706,\n \"acc_norm_stderr\": 0.03491207857486517\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\
: 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6484848484848484,\n \"acc_stderr\": 0.037282069986826503,\n\
\ \"acc_norm\": 0.6484848484848484,\n \"acc_norm_stderr\": 0.037282069986826503\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6767676767676768,\n \"acc_stderr\": 0.033322999210706444,\n \"\
acc_norm\": 0.6767676767676768,\n \"acc_norm_stderr\": 0.033322999210706444\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7098445595854922,\n \"acc_stderr\": 0.032752644677915166,\n\
\ \"acc_norm\": 0.7098445595854922,\n \"acc_norm_stderr\": 0.032752644677915166\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4641025641025641,\n \"acc_stderr\": 0.02528558599001784,\n \
\ \"acc_norm\": 0.4641025641025641,\n \"acc_norm_stderr\": 0.02528558599001784\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \
\ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.46218487394957986,\n \"acc_stderr\": 0.032385469487589795,\n\
\ \"acc_norm\": 0.46218487394957986,\n \"acc_norm_stderr\": 0.032385469487589795\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7045871559633028,\n \"acc_stderr\": 0.019560619182976,\n \"acc_norm\"\
: 0.7045871559633028,\n \"acc_norm_stderr\": 0.019560619182976\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.3472222222222222,\n\
\ \"acc_stderr\": 0.032468872436376486,\n \"acc_norm\": 0.3472222222222222,\n\
\ \"acc_norm_stderr\": 0.032468872436376486\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.034107853389047205,\n\
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.034107853389047205\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6624472573839663,\n \"acc_stderr\": 0.030781549102026223,\n \
\ \"acc_norm\": 0.6624472573839663,\n \"acc_norm_stderr\": 0.030781549102026223\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.600896860986547,\n\
\ \"acc_stderr\": 0.03286745312567961,\n \"acc_norm\": 0.600896860986547,\n\
\ \"acc_norm_stderr\": 0.03286745312567961\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.04225875451969638,\n\
\ \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.04225875451969638\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6776859504132231,\n \"acc_stderr\": 0.042664163633521685,\n \"\
acc_norm\": 0.6776859504132231,\n \"acc_norm_stderr\": 0.042664163633521685\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6574074074074074,\n\
\ \"acc_stderr\": 0.045879047413018105,\n \"acc_norm\": 0.6574074074074074,\n\
\ \"acc_norm_stderr\": 0.045879047413018105\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\
\ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\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\": 0.6990291262135923,\n \"acc_stderr\": 0.045416094465039476,\n\
\ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.045416094465039476\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.811965811965812,\n\
\ \"acc_stderr\": 0.025598193686652265,\n \"acc_norm\": 0.811965811965812,\n\
\ \"acc_norm_stderr\": 0.025598193686652265\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7113665389527458,\n\
\ \"acc_stderr\": 0.016203792703197786,\n \"acc_norm\": 0.7113665389527458,\n\
\ \"acc_norm_stderr\": 0.016203792703197786\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5346820809248555,\n \"acc_stderr\": 0.026854257928258893,\n\
\ \"acc_norm\": 0.5346820809248555,\n \"acc_norm_stderr\": 0.026854257928258893\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33631284916201115,\n\
\ \"acc_stderr\": 0.015801003729145894,\n \"acc_norm\": 0.33631284916201115,\n\
\ \"acc_norm_stderr\": 0.015801003729145894\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5228758169934641,\n \"acc_stderr\": 0.028599936776089782,\n\
\ \"acc_norm\": 0.5228758169934641,\n \"acc_norm_stderr\": 0.028599936776089782\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6141479099678456,\n\
\ \"acc_stderr\": 0.027648149599751468,\n \"acc_norm\": 0.6141479099678456,\n\
\ \"acc_norm_stderr\": 0.027648149599751468\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6265432098765432,\n \"acc_stderr\": 0.02691500301138016,\n\
\ \"acc_norm\": 0.6265432098765432,\n \"acc_norm_stderr\": 0.02691500301138016\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3617021276595745,\n \"acc_stderr\": 0.028663820147199495,\n \
\ \"acc_norm\": 0.3617021276595745,\n \"acc_norm_stderr\": 0.028663820147199495\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3578878748370274,\n\
\ \"acc_stderr\": 0.012243563850490313,\n \"acc_norm\": 0.3578878748370274,\n\
\ \"acc_norm_stderr\": 0.012243563850490313\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.03016191193076711,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.03016191193076711\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.49836601307189543,\n \"acc_stderr\": 0.020227726838150117,\n \
\ \"acc_norm\": 0.49836601307189543,\n \"acc_norm_stderr\": 0.020227726838150117\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.04494290866252089,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.04494290866252089\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6081632653061224,\n \"acc_stderr\": 0.031251275910891656,\n\
\ \"acc_norm\": 0.6081632653061224,\n \"acc_norm_stderr\": 0.031251275910891656\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6766169154228856,\n\
\ \"acc_stderr\": 0.03307615947979033,\n \"acc_norm\": 0.6766169154228856,\n\
\ \"acc_norm_stderr\": 0.03307615947979033\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036624,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036624\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.42168674698795183,\n\
\ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n\
\ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7134502923976608,\n \"acc_stderr\": 0.03467826685703826,\n\
\ \"acc_norm\": 0.7134502923976608,\n \"acc_norm_stderr\": 0.03467826685703826\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3108935128518972,\n\
\ \"mc1_stderr\": 0.016203316673559693,\n \"mc2\": 0.4616473915095851,\n\
\ \"mc2_stderr\": 0.014431098139511664\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7371744277821626,\n \"acc_stderr\": 0.01237092252726201\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.047763457164518575,\n \
\ \"acc_stderr\": 0.00587438753622932\n }\n}\n```"
repo_url: https://huggingface.co/Severian/ANIMA-Nectar-v3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|arc:challenge|25_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|gsm8k|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hellaswag|10_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-02-02.105784.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T16-02-02.105784.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- '**/details_harness|winogrande|5_2023-12-09T16-02-02.105784.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-09T16-02-02.105784.parquet'
- config_name: results
data_files:
- split: 2023_12_09T16_02_02.105784
path:
- results_2023-12-09T16-02-02.105784.parquet
- split: latest
path:
- results_2023-12-09T16-02-02.105784.parquet
---
# Dataset Card for Evaluation run of Severian/ANIMA-Nectar-v3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Severian/ANIMA-Nectar-v3
- **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 [Severian/ANIMA-Nectar-v3](https://huggingface.co/Severian/ANIMA-Nectar-v3) 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_Severian__ANIMA-Nectar-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-09T16:02:02.105784](https://huggingface.co/datasets/open-llm-leaderboard/details_Severian__ANIMA-Nectar-v3/blob/main/results_2023-12-09T16-02-02.105784.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": 0.5279231991868109,
"acc_stderr": 0.034095739528534785,
"acc_norm": 0.5365465936132023,
"acc_norm_stderr": 0.0349316183151297,
"mc1": 0.3108935128518972,
"mc1_stderr": 0.016203316673559693,
"mc2": 0.4616473915095851,
"mc2_stderr": 0.014431098139511664
},
"harness|arc:challenge|25": {
"acc": 0.454778156996587,
"acc_stderr": 0.014551507060836353,
"acc_norm": 0.4948805460750853,
"acc_norm_stderr": 0.014610624890309154
},
"harness|hellaswag|10": {
"acc": 0.5621390161322446,
"acc_stderr": 0.004951097802775953,
"acc_norm": 0.7599083847839075,
"acc_norm_stderr": 0.004262659388824526
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4888888888888889,
"acc_stderr": 0.04318275491977976,
"acc_norm": 0.4888888888888889,
"acc_norm_stderr": 0.04318275491977976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.506578947368421,
"acc_stderr": 0.040685900502249704,
"acc_norm": 0.506578947368421,
"acc_norm_stderr": 0.040685900502249704
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
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"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5962264150943396,
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"acc_norm": 0.5962264150943396,
"acc_norm_stderr": 0.03019761160019795
},
"harness|hendrycksTest-college_biology|5": {
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
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"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
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},
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"acc": 0.5433526011560693,
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"acc_norm": 0.5433526011560693,
"acc_norm_stderr": 0.03798106566014498
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.30392156862745096,
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"acc_norm": 0.30392156862745096,
"acc_norm_stderr": 0.04576665403207763
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
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"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4723404255319149,
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},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.37719298245614036,
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"acc_norm": 0.37719298245614036,
"acc_norm_stderr": 0.04559522141958216
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5103448275862069,
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"acc_norm": 0.5103448275862069,
"acc_norm_stderr": 0.04165774775728763
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3835978835978836,
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"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3253968253968254,
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6258064516129033,
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"acc_norm_stderr": 0.027528904299845704
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.43842364532019706,
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"acc_norm_stderr": 0.03491207857486517
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.58,
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"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6484848484848484,
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},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6767676767676768,
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7098445595854922,
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"acc_norm": 0.7098445595854922,
"acc_norm_stderr": 0.032752644677915166
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4641025641025641,
"acc_stderr": 0.02528558599001784,
"acc_norm": 0.4641025641025641,
"acc_norm_stderr": 0.02528558599001784
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.28888888888888886,
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"acc_norm": 0.28888888888888886,
"acc_norm_stderr": 0.027634907264178544
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.46218487394957986,
"acc_stderr": 0.032385469487589795,
"acc_norm": 0.46218487394957986,
"acc_norm_stderr": 0.032385469487589795
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
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"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.0395802723112157
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7045871559633028,
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"acc_norm": 0.7045871559633028,
"acc_norm_stderr": 0.019560619182976
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3472222222222222,
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"acc_norm": 0.3472222222222222,
"acc_norm_stderr": 0.032468872436376486
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6176470588235294,
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},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6624472573839663,
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"acc_norm": 0.6624472573839663,
"acc_norm_stderr": 0.030781549102026223
},
"harness|hendrycksTest-human_aging|5": {
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},
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},
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},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6687116564417178,
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},
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},
"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5346820809248555,
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.33631284916201115,
"acc_stderr": 0.015801003729145894,
"acc_norm": 0.33631284916201115,
"acc_norm_stderr": 0.015801003729145894
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5228758169934641,
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"acc_norm": 0.5228758169934641,
"acc_norm_stderr": 0.028599936776089782
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6141479099678456,
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"harness|hendrycksTest-prehistory|5": {
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},
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},
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},
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},
"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7134502923976608,
"acc_stderr": 0.03467826685703826,
"acc_norm": 0.7134502923976608,
"acc_norm_stderr": 0.03467826685703826
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3108935128518972,
"mc1_stderr": 0.016203316673559693,
"mc2": 0.4616473915095851,
"mc2_stderr": 0.014431098139511664
},
"harness|winogrande|5": {
"acc": 0.7371744277821626,
"acc_stderr": 0.01237092252726201
},
"harness|gsm8k|5": {
"acc": 0.047763457164518575,
"acc_stderr": 0.00587438753622932
}
}
```
### 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] |
wdndev/webnovel-chinese | ---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
tags:
- llm
- pretrain
size_categories:
- 1B<n<10B
---
## 简介
搜集网络上的网文小说，清洗，分割后，用于训练大语言模型，共计9000本左右，大约9B左右token。
## 使用
### 格式说明
采用`jsonl`格式存储，分为三个字段：
- `title` ：小说名称
- `chapter`：章节
- `text`：正文内容
示例：
```json
{"title": "斗破苍穹", "chapter": " 第一章 陨落的天才", "text": "“斗之力，三段！”\n望着测验魔石碑上面闪亮得甚至有些刺眼的五个大字，少年面无表情，唇角有着一抹自嘲，紧握的手掌，因为大力，而导致略微尖锐的指甲深深的刺进了掌心之中，带来一阵阵钻心的疼痛……\n“萧炎，斗之力，三段！级别：低级！”测验魔石碑之旁，一位中年男子，看了一眼碑上所显示出来的信息，语气漠然的将之公布了出来……\n"}
```
|
MohammadHarrisCallME/_NetProgrammingBasics | ---
license: llama2
---
|
joey234/mmlu-logical_fallacies-original-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: 17815
num_examples: 35
download_size: 14481
dataset_size: 17815
---
# Dataset Card for "mmlu-logical_fallacies-original-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
peterpanpan/stackoverflow-kubernetes-questions | ---
license: apache-2.0
---
covert from `https://huggingface.co/datasets/mcipriano/stackoverflow-kubernetes-questions/blob/main/README.md`
format from parquet to csv
coverting code as below
```
import pandas as pd
from pandas import read_parquet
data = read_parquet("~/Downloads/kubernetes_dump.parquet")
#print(data.count())
#data.head()
data.to_csv('/tmp/out.csv', index=False)
``` |
AIVOICES123424/yuri | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 352874.0
num_examples: 1
download_size: 304174
dataset_size: 352874.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Shivansh2310/trinity-dolly-10k | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 16392818
num_examples: 10000
download_size: 10078470
dataset_size: 16392818
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "trinity-dolly-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HES-XPLAIN/SportsImageClassificationOld | ---
task_categories:
- image-classification
language:
- en
tags:
- sports
size_categories:
- 100M<n<1B
--- |
distilled-one-sec-cv12-each-chunk-uniq/chunk_82 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1365922856.0
num_examples: 266158
download_size: 1398238480
dataset_size: 1365922856.0
---
# Dataset Card for "chunk_82"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_cola_who_which | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 2049
num_examples: 23
- name: test
num_bytes: 1406
num_examples: 17
- name: train
num_bytes: 22193
num_examples: 245
download_size: 17836
dataset_size: 25648
---
# Dataset Card for "MULTI_VALUE_cola_who_which"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/wikitext-103-raw-v1-sent-permute-3 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2181716652
num_examples: 7205397
- name: validation
num_bytes: 1159288
num_examples: 3760
- name: test
num_bytes: 1305088
num_examples: 4358
download_size: 1264108325
dataset_size: 2184181028
---
# Dataset Card for "wikitext-103-raw-v1-sent-permute-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ted_multi | ---
pretty_name: TEDMulti
paperswithcode_id: null
dataset_info:
features:
- name: translations
dtype:
translation_variable_languages:
languages:
- ar
- az
- be
- bg
- bn
- bs
- calv
- cs
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fr-ca
- gl
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ka
- kk
- ko
- ku
- lt
- mk
- mn
- mr
- ms
- my
- nb
- nl
- pl
- pt
- pt-br
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- ta
- th
- tr
- uk
- ur
- vi
- zh
- zh-cn
- zh-tw
num_languages: 60
- name: talk_name
dtype: string
config_name: plain_text
splits:
- name: test
num_bytes: 23364983
num_examples: 7213
- name: train
num_bytes: 748209995
num_examples: 258098
- name: validation
num_bytes: 19435383
num_examples: 6049
download_size: 352222045
dataset_size: 791010361
---
# Dataset Card for "ted_multi"
## 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://github.com/neulab/word-embeddings-for-nmt](https://github.com/neulab/word-embeddings-for-nmt)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 352.23 MB
- **Size of the generated dataset:** 791.01 MB
- **Total amount of disk used:** 1.14 GB
### Dataset Summary
Massively multilingual (60 language) data set derived from TED Talk transcripts.
Each record consists of parallel arrays of language and text. Missing and
incomplete translations will be filtered out.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 352.23 MB
- **Size of the generated dataset:** 791.01 MB
- **Total amount of disk used:** 1.14 GB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"talk_name": "shabana_basij_rasikh_dare_to_educate_afghan_girls",
"translations": "{\"language\": [\"ar\", \"az\", \"bg\", \"bn\", \"cs\", \"da\", \"de\", \"el\", \"en\", \"es\", \"fa\", \"fr\", \"he\", \"hi\", \"hr\", \"hu\", \"hy\", \"id\", \"it\", ..."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `translations`: a multilingual `string` variable, with possible languages including `ar`, `az`, `be`, `bg`, `bn`.
- `talk_name`: a `string` feature.
### Data Splits
| name |train |validation|test|
|----------|-----:|---------:|---:|
|plain_text|258098| 6049|7213|
## 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{qi-EtAl:2018:N18-2,
author = {Qi, Ye and Sachan, Devendra and Felix, Matthieu and Padmanabhan, Sarguna and Neubig, Graham},
title = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},
booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
month = {June},
year = {2018},
address = {New Orleans, Louisiana},
publisher = {Association for Computational Linguistics},
pages = {529--535},
abstract = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},
url = {http://www.aclweb.org/anthology/N18-2084}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
usvsnsp/deduped-num-frequencies | ---
dataset_info:
features:
- name: TokenID
dtype: int64
- name: Frequency
dtype: int64
splits:
- name: memorized
num_bytes: 960000
num_examples: 60000
- name: non_memorized
num_bytes: 960000
num_examples: 60000
- name: total
num_bytes: 960000
num_examples: 60000
download_size: 1974196
dataset_size: 2880000
---
# Dataset Card for "deduped-num-frequencies"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Om007/kendal_bot | ---
task_categories:
- question-answering
language:
- en
---
# Dataset Card for Kendal
<!-- Provide a quick summary of the dataset. -->
This is a dataset of for Kendal Bot.
## 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] |
xppast/voice | ---
license: mit
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcripts
dtype: string
splits:
- name: train
num_bytes: 4785859.5
num_examples: 33
- name: test
num_bytes: 1138202.1666666667
num_examples: 5
- name: valid
num_bytes: 895347.3333333334
num_examples: 4
download_size: 6710917
dataset_size: 6819409.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
DBQ/Prada.Product.prices.Germany | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Germany - Prada - Product-level price list
tags:
- webscraping
- ecommerce
- Prada
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1315247
num_examples: 2588
download_size: 373618
dataset_size: 1315247
---
# Prada web scraped data
## About the website
The **Luxury Fashion Industry** in the **EMEA** region, particularly in **Germany**, has experienced significant transformation in recent years. The face of the industry is continuously changing, with fashion houses like **Prada** sitting at the forefront of this evolution. With an increasing number of consumers turning to online platforms for shopping, the digitalization process has been greatly accelerated for high-end fashion labels. The **ecommerce** sector has thus become instrumental in driving sales. The dataset observed includes **Ecommerce product-list page (PLP) data** on **Prada** in Germany, offering a fresh perspective on buying habits, consumer behaviour, and fashion trends within this evolving industry in the region.
## Link to **dataset**
[Germany - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Germany/r/recTwfXDGd6805PNU)
|
richwardle/reduced-imagenet | ---
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- image-feature-extraction
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 2156767982.0
num_examples: 26000
download_size: 2183967663
dataset_size: 2156767982.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Imagenet Mini Dataset
This dataset is a subset of the Imagenet validation set containing 26,000 images. It has been curated to have equal class distributions, with 26 randomly sampled images from each class.
All images have been resized to (224, 224) pixels, and are in RGB format.
## Citation
If you use this dataset in your research, please cite the original Imagenet dataset:
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248–255).
|
linqus/tokenized-codeparrot-ds-small | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 708311652
num_examples: 1372697
- name: valid
num_bytes: 7259088
num_examples: 14068
download_size: 313854357
dataset_size: 715570740
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
---
|
bethgelab/frequency_determines_performance | ---
license: mit
task_categories:
- zero-shot-classification
- feature-extraction
language:
- en
size_categories:
- n<1K
---
**Frequency estimation results and tagged samples:** `counts_and_indices.zip` contains all the result jsons (for the estimated frequencies for image-only, text-only and image-text searches) and the sample indices that are tagged to each concept for the LAION400m/LAION-Aesthetics datasets.
**Constructed dictionaries and other pretraining and downstream data artefacts:** Due to the large size of all our data artefacts, we release our dictionaries and other feature artefacts as split files of a 110GB sized zip file (named as `features_zip_part_aa`, `features_zip_part_ab` and `features_zip_part_ac`). Please download the individual split files, and then manually combine them to reconstruct the original zip file like this:
```bash
cat features_zip_part_aa features_zip_part_ab features_zip_part_ac > features.zip
```
Once combined, please verify that the file is correctly transferred by comparing the md5sum hash of the file:
```bash
md5sum features.zip
```
The output hash should be: `11f6339df3206257efdfc4a54dd7ca60 features.zip`
For more details, see our [github repository](https://github.com/bethgelab/frequency_determines_performance) |
thefrankhsu/hate_speech_twitter | ---
task_categories:
- text-classification
language:
- en
tags:
- health
- tweet
- hate speech
- mental health
- hate speech detection
- hate speech classification
- social media
- mobile health
size_categories:
- 1K<n<10K
---
## Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
The dataset is designed to analyze and address hate speech within online platforms. It consists of two sets: the training and testing sets. The two datasets have been labeled and categorized instances of hate speech into nine distinct categories.
## Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
The dataset comprises three key features: tweets, labels (with hate speech denoted as 1 and non-hate speech as 0), and categories (behavior, class, disability, ethnicity, gender,
physical appearance, race, religion, sexual orientation).
* Training set: contains a total of 5679 tweets (Hate Speech: 1516 / Non Hate Speech: 4163), and the number of hate speech in each category is not equally distributed.
* Testing set: contains a total of 1000 tweets (Hate Speech: 500 / Non Hate Speech: 500), and the number of hate speech in each category is generally even.
## Uses
This dataset can be utilized for various purposes, including but not limited to:
* Developing and training machine learning models for hate speech detection.
* Analyzing the prevalence and patterns of hate speech across different categories.
* Understanding the challenges associated with categorizing hate speech on social media platforms.
Check it out for the example [project](https://github.com/Wei-Hsi/AI4health)!
## Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The dataset utilized in this study is sourced from Kaggle and named the [Hate Speech and Offensive Language dataset](https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offensive-language-dataset/).
Hate speech instances are identified by selecting tweets within the "class" column.
## Annotations
<!-- 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. -->
Category labels were generated through an OpenAI API call employing the GPT-3.5 model.
It's important to note the instability in category predictions when utilizing GPT-3.5 for label generation, as it tends to predict different categories each time. However, we have confirmed that these tweets were labeled correctly. If there are any misclassified labels, please feel free to reach out. Thank you in advance for your assistance.
## Dataset Card Contact
Please feel free to contact me via wh476@cornell.edu! |
mixamrepijey/gorilla-hf | ---
license: apache-2.0
---
|
Lostkyd/InstrucData | ---
dataset_info:
features:
- name: Instruction
dtype: string
- name: Input
dtype: string
- name: Output
dtype: string
splits:
- name: train
num_bytes: 173299
num_examples: 90
download_size: 40079
dataset_size: 173299
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/mujina_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of mujina/ムジナ/貉SSSS (Azur Lane)
This is the dataset of mujina/ムジナ/貉SSSS (Azur Lane), containing 283 images and their tags.
The core tags of this character are `short_hair, breasts, blue_eyes, large_breasts, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 283 | 368.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mujina_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 283 | 198.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mujina_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 693 | 420.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mujina_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 283 | 325.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mujina_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 693 | 606.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mujina_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mujina_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, corset, military_jacket, purple_shorts, solo, white_gloves, white_jacket, looking_at_viewer, short_necktie, thighs, purple_necktie, underbust, white_background, white_shirt, short_shorts, collared_shirt, long_sleeves, open_jacket, simple_background, white_footwear, blush |
| 1 | 8 |  |  |  |  |  | 1girl, cleavage, purple_bikini, solo, thighs, bare_shoulders, blush, looking_at_viewer, navel, collarbone, beach, blue_sky, day, ocean, outdoors, pink_hair, sitting, wet, cloud, crossed_legs, hair_between_eyes |
| 2 | 16 |  |  |  |  |  | 1girl, cleavage, navel, solo, looking_at_viewer, purple_bikini, white_background, brown_hair, simple_background, thighs, bare_shoulders, sitting, wet |
| 3 | 36 |  |  |  |  |  | 1girl, hetero, 1boy, blush, penis, solo_focus, nipples, open_mouth, sex, mosaic_censoring, completely_nude, vaginal, collarbone, pussy, sweat, navel, looking_at_viewer, spread_legs, thighs, cum, brown_hair, lying, hair_between_eyes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | corset | military_jacket | purple_shorts | solo | white_gloves | white_jacket | looking_at_viewer | short_necktie | thighs | purple_necktie | underbust | white_background | white_shirt | short_shorts | collared_shirt | long_sleeves | open_jacket | simple_background | white_footwear | blush | cleavage | purple_bikini | bare_shoulders | navel | collarbone | beach | blue_sky | day | ocean | outdoors | pink_hair | sitting | wet | cloud | crossed_legs | hair_between_eyes | brown_hair | hetero | 1boy | penis | solo_focus | nipples | open_mouth | sex | mosaic_censoring | completely_nude | vaginal | pussy | sweat | spread_legs | cum | lying |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:------------------|:----------------|:-------|:---------------|:---------------|:--------------------|:----------------|:---------|:-----------------|:------------|:-------------------|:--------------|:---------------|:-----------------|:---------------|:--------------|:--------------------|:-----------------|:--------|:-----------|:----------------|:-----------------|:--------|:-------------|:--------|:-----------|:------|:--------|:-----------|:------------|:----------|:------|:--------|:---------------|:--------------------|:-------------|:---------|:-------|:--------|:-------------|:----------|:-------------|:------|:-------------------|:------------------|:----------|:--------|:--------|:--------------|:------|:--------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | | | | X | | | X | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 2 | 16 |  |  |  |  |  | X | | | | X | | | X | | X | | | X | | | | | | X | | | X | X | X | X | | | | | | | | X | X | | | | X | | | | | | | | | | | | | | | |
| 3 | 36 |  |  |  |  |  | X | | | | | | | X | | X | | | | | | | | | | | X | | | | X | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
joachimsallstrom/mjportraits_and_blurred | ---
license: creativeml-openrail-m
---
|
manishh16/crack | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 249182453.0
num_examples: 31
download_size: 22493785
dataset_size: 249182453.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mcemilg/turkish-plu-goal-inference | ---
task_categories:
- text-classification
language:
- tr
size_categories:
- 100K<n<1M
---
Homepage: https://github.com/GGLAB-KU/turkish-plu
|
theBrokenCat/EdificioPereda | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 452640209.0
num_examples: 64
download_size: 444672075
dataset_size: 452640209.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_mnli_regularized_reflexives_aave | ---
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: 19251
num_examples: 94
- name: dev_mismatched
num_bytes: 23121
num_examples: 87
- name: test_matched
num_bytes: 21604
num_examples: 90
- name: test_mismatched
num_bytes: 20670
num_examples: 82
- name: train
num_bytes: 936051
num_examples: 3883
download_size: 578604
dataset_size: 1020697
---
# Dataset Card for "MULTI_VALUE_mnli_regularized_reflexives_aave"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bengaliAI/cvbn | ---
license: cc
---
|
zhan1993/ARB_transfer_matrix_v2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: expert_name
dtype: string
- name: task_eval_on
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 15849
num_examples: 356
download_size: 10642
dataset_size: 15849
---
# Dataset Card for "ARB_transfer_matrix_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Serverless/dev_mode-wtq | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: WikiTableQuestions-wtq
size_categories:
- 10K<n<100K
source_datasets:
- wikitablequestions
task_categories:
- question-answering
task_ids: []
tags:
- table-question-answering
---
# Dataset Card for dev_mode-wtq
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [WikiTableQuestions homepage](https://nlp.stanford.edu/software/sempre/wikitable)
- **Repository:** [WikiTableQuestions repository](https://github.com/ppasupat/WikiTableQuestions)
- **Paper:** [Compositional Semantic Parsing on Semi-Structured Tables](https://arxiv.org/abs/1508.00305)
- **Leaderboard:** [WikiTableQuestions leaderboard on PaperWithCode](https://paperswithcode.com/dataset/wikitablequestions)
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The dev_mode-wtq dataset is a small-scale dataset for the task of question answering on semi-structured tables.
This data includes the `aggregation_label` and `answer_coordinates` to make it easy to train this model on any [TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas#usage-finetuning) based modles.
### Supported Tasks and Leaderboards
question-answering, table-question-answering
### Languages
en
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 27.91 MB
- **Size of the generated dataset:** 45.68 MB
- **Total amount of disk used:** 73.60 MB
An example of 'validation' looks as follows:
```
{
"id": "nt-0",
"question": "What is the duration for the last invocation?",
"answers": [
"340 ms"
],
"table": {
"header": [
"recent",
"type",
"spans",
"logs",
"errors",
"warnings",
"duration",
"resource"
],
"rows": [
[
"1",
"span",
"1",
"1",
"1",
"2",
"340 ms",
"aws-lambda-typescript-express-dev-express"
]
]
}
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a `list` of `string` feature.
- `answers_coordinates`: a `list` of `int,int` tuples.
- `aggregation_label`: a `string` feature.
- `table`: a dictionary feature containing:
- `header`: a `list` of `string` features.
- `rows`: a `list` of `list` of `string` features:
- `name`: a `string` feature.
### Data Splits
TBA
## 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
Panupong Pasupat and Percy Liang
### Licensing Information
Creative Commons Attribution Share Alike 4.0 International
### Citation Information
```
@inproceedings{pasupat-liang-2015-compositional,
title = "Compositional Semantic Parsing on Semi-Structured Tables",
author = "Pasupat, Panupong and Liang, Percy",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-1142",
doi = "10.3115/v1/P15-1142",
pages = "1470--1480",
}
```
### Contributions
Thanks to [@SivilTaram](https://github.com/SivilTaram) for adding this dataset. |
dariatsisar/sample_dataset | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': science/technology
'1': travel
'2': politics
'3': health
splits:
- name: train
num_bytes: 104902
num_examples: 394
- name: test
num_bytes: 24642
num_examples: 99
download_size: 76286
dataset_size: 129544
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
amktk/ktkDataSet | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transctiption
dtype: string
splits:
- name: train
num_bytes: 71647032.0
num_examples: 10
download_size: 60508649
dataset_size: 71647032.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ktkDataSet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PrabhaB/guanaco-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966692
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Cohere/wikipedia-22-12-zh-embeddings | ---
language:
- zh
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (zh) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (zh)](https://zh.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),
You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Loading the dataset
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)
for doc in docs:
docid = doc['id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
A full search example:
```python
#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere
co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com
#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = torch.tensor(doc_embeddings)
query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings
query_embedding = torch.tensor(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "\n")
```
## Performance
You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance) |
xedwin23x/StanfordCars | ---
license: unknown
---
|
mboth/luftBereitstellen-100-undersampled | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: Datatype
dtype: string
- name: Beschreibung
dtype: string
- name: Name
dtype: string
- name: Unit
dtype: string
- name: text
dtype: string
- name: Grundfunktion
dtype: string
- name: ZweiteGrundfunktion
dtype: string
- name: label
dtype:
class_label:
names:
'0': AbluftAllgemein
'1': Abluftfilter
'2': Abluftklappe
'3': Abluftventilator
'4': Außenluftfilter
'5': Außenluftklappe
'6': Befeuchter
'7': Erhitzer
'8': Filter
'9': Fortluftklappe
'10': GerätAllgemein
'11': Kaeltemengenzaehler
'12': KlappenAllgemein
'13': Kühler
'14': Regler
'15': Umluft
'16': Ventilator
'17': Wärmemengenzähler
'18': Wärmerückgewinnung
'19': ZuluftAllgemein
'20': Zuluftfilter
'21': Zuluftklappe
'22': Zuluftventilator
splits:
- name: train
num_bytes: 378107.292848404
num_examples: 1778
- name: test
num_bytes: 238179
num_examples: 1124
- name: valid
num_bytes: 238179
num_examples: 1124
download_size: 280245
dataset_size: 854465.292848404
---
# Dataset Card for "luftBereitstellen-100-undersampled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ben-epstein/amazon_polarity_10_pct | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 163359702
num_examples: 360000
- name: test
num_bytes: 18182813
num_examples: 40000
download_size: 120691417
dataset_size: 181542515
---
# Amazon Polarity 10pct
This is a direct subset of the original [Amazon Polarity](https://huggingface.co/datasets/amazon_polarity) dataset, downsampled 10pct with a random shuffle
### Dataset Summary
For quicker testing on Amazon Polarity. See https://huggingface.co/datasets/amazon_polarity for details and attributions
### Source Data
```python
from datasets import ClassLabel, Dataset, DatasetDict, load_dataset
ds_full = load_dataset("amazon_polarity", streaming=True)
ds_train_10_pct = Dataset.from_list(list(ds_full["train"].shuffle(seed=42).take(360_000)))
ds_test_10_pct = Dataset.from_list(list(ds_full["test"].shuffle(seed=42).take(40_000)))
ds_10_pct = DatasetDict({"train": ds_train_10_pct, "test": ds_test_10_pct})
# Need to recreate the class labels
class_label = ClassLabel(num_classes=2, names=["negative", "positive"])
ds_10_pct = ds_10_pct.map(lambda row: {"title": row["title"], "content": row["content"], "label": "negative" if not row["label"] else "positive"})
ds_10_pct = ds_10_pct.cast_column("label", class_label)
```
|
aisc-team-a1/guidelines-qa-finetuning | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2053169
num_examples: 75
download_size: 511694
dataset_size: 2053169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gagan3012/areta_v3 | ---
dataset_info:
features:
- name: text
sequence: string
- name: detect_tags
sequence: string
- name: correct_tags
sequence: string
- name: len_text
dtype: int64
- name: len_detect_tags
dtype: int64
- name: len_correct_tags
dtype: int64
splits:
- name: train
num_bytes: 96930716
num_examples: 100000
- name: validation
num_bytes: 1986694
num_examples: 1017
download_size: 19852500
dataset_size: 98917410
---
# Dataset Card for "areta_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Chunt0/paul_price | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 10352711.0
num_examples: 41
download_size: 10344553
dataset_size: 10352711.0
---
# Dataset Card for "paul_price"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vilsonrodrigues/lfw | ---
license: apache-2.0
---
Samples from the LFW dataset. Samples where there is one more face per user were selected. They were then partitioned into two directories: ingestion and recovery. This was done to test a facial recognition system. |
arthurMM801/cnh-rg-cpf | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': cnh_completo
'1': cpf_completo
'2': rg_completo
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 16204820.0
num_examples: 48
- name: test
num_bytes: 4901507.0
num_examples: 12
download_size: 21109239
dataset_size: 21106327.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
amcoff/recept | ---
annotations_creators:
- no-annotation
language:
- sv
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: Recept
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for Recept
### Dataset Summary
[More Information Needed]
### 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]
|
Jaskirat-04/fine-tunning | ---
license: mit
---
|
heliosprime/twitter_dataset_1713167560 | ---
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: 3203
num_examples: 9
download_size: 8354
dataset_size: 3203
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713167560"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_jxhong__CAlign-alpaca-7b | ---
pretty_name: Evaluation run of jxhong/CAlign-alpaca-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jxhong/CAlign-alpaca-7b](https://huggingface.co/jxhong/CAlign-alpaca-7b) 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_jxhong__CAlign-alpaca-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T14:18:50.060462](https://huggingface.co/datasets/open-llm-leaderboard/details_jxhong__CAlign-alpaca-7b/blob/main/results_2023-09-23T14-18-50.060462.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\": 0.1967281879194631,\n\
\ \"em_stderr\": 0.0040710291374288195,\n \"f1\": 0.2515457214765097,\n\
\ \"f1_stderr\": 0.004085507734234057,\n \"acc\": 0.36712327209690443,\n\
\ \"acc_stderr\": 0.007903286807442752\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.1967281879194631,\n \"em_stderr\": 0.0040710291374288195,\n\
\ \"f1\": 0.2515457214765097,\n \"f1_stderr\": 0.004085507734234057\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.013646702047005308,\n \
\ \"acc_stderr\": 0.003195747075480819\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7205998421468035,\n \"acc_stderr\": 0.012610826539404686\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jxhong/CAlign-alpaca-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: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|arc:challenge|25_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T14_18_50.060462
path:
- '**/details_harness|drop|3_2023-09-23T14-18-50.060462.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T14-18-50.060462.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T14_18_50.060462
path:
- '**/details_harness|gsm8k|5_2023-09-23T14-18-50.060462.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T14-18-50.060462.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hellaswag|10_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T20:26:06.755216.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T20:26:06.755216.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T20:26:06.755216.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T14_18_50.060462
path:
- '**/details_harness|winogrande|5_2023-09-23T14-18-50.060462.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T14-18-50.060462.parquet'
- config_name: results
data_files:
- split: 2023_08_09T20_26_06.755216
path:
- results_2023-08-09T20:26:06.755216.parquet
- split: 2023_09_23T14_18_50.060462
path:
- results_2023-09-23T14-18-50.060462.parquet
- split: latest
path:
- results_2023-09-23T14-18-50.060462.parquet
---
# Dataset Card for Evaluation run of jxhong/CAlign-alpaca-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jxhong/CAlign-alpaca-7b
- **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 [jxhong/CAlign-alpaca-7b](https://huggingface.co/jxhong/CAlign-alpaca-7b) 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_jxhong__CAlign-alpaca-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T14:18:50.060462](https://huggingface.co/datasets/open-llm-leaderboard/details_jxhong__CAlign-alpaca-7b/blob/main/results_2023-09-23T14-18-50.060462.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": 0.1967281879194631,
"em_stderr": 0.0040710291374288195,
"f1": 0.2515457214765097,
"f1_stderr": 0.004085507734234057,
"acc": 0.36712327209690443,
"acc_stderr": 0.007903286807442752
},
"harness|drop|3": {
"em": 0.1967281879194631,
"em_stderr": 0.0040710291374288195,
"f1": 0.2515457214765097,
"f1_stderr": 0.004085507734234057
},
"harness|gsm8k|5": {
"acc": 0.013646702047005308,
"acc_stderr": 0.003195747075480819
},
"harness|winogrande|5": {
"acc": 0.7205998421468035,
"acc_stderr": 0.012610826539404686
}
}
```
### 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] |
AdapterOcean/code_instructions_standardized_cluster_12_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 13843792
num_examples: 7746
download_size: 7130413
dataset_size: 13843792
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "code_instructions_standardized_cluster_12_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-billsum-default-258166-2318473352 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- billsum
eval_info:
task: summarization
model: Artifact-AI/t5_base_courtlistener_billsum
metrics: []
dataset_name: billsum
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Artifact-AI/t5_base_courtlistener_billsum
* Dataset: billsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Artifact-AI](https://huggingface.co/Artifact-AI) for evaluating this model. |
nlpUc3mStudents/mental-risk-b | ---
dataset_info:
features:
- name: subject_id
dtype: string
- name: id_message
dtype: int64
- name: date
dtype: string
- name: message
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 800039
num_examples: 6248
- name: test
num_bytes: 76071
num_examples: 624
download_size: 475767
dataset_size: 876110
---
# Dataset Card for "mental-risk-b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DepositorOP/masterstack | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
dtype: float64
splits:
- name: test
num_bytes: 151727.48160821214
num_examples: 702
- name: train
num_bytes: 1364250.5183917878
num_examples: 6312
download_size: 1016008
dataset_size: 1515978.0
---
# Dataset Card for "masterstack"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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