datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
Parikshith/gold-ha-en-train-scored | Parikshith | 2023-12-05T00:36:03Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T00:36:02Z | ---
dataset_info:
features:
- name: mt
dtype: string
- name: src
dtype: string
- name: score_wmt20-comet-qe-da
dtype: float64
- name: score_wmt21-comet-qe-da
dtype: float64
splits:
- name: train
num_bytes: 2983656
num_examples: 9818
download_size: 1836593
dataset_size: 2983656
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
mhzarem76/fintuned-llm | mhzarem76 | 2023-12-05T00:46:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T00:46:14Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 18997844
num_examples: 51942
download_size: 11986973
dataset_size: 18997844
---
# Dataset Card for "fintuned-llm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
AdamKolt/FRCDojo | AdamKolt | 2023-12-05T00:47:25Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-05T00:47:25Z | ---
license: mit
---
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|
bharathmuppa/BAK | bharathmuppa | 2023-12-05T00:55:07Z | 0 | 0 | [
"license:gpl-3.0",
"region:us"
]
| null | 2023-12-05T00:55:07Z | ---
license: gpl-3.0
---
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|
Carlosgg14/leorio | Carlosgg14 | 2023-12-05T00:55:38Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T00:55:13Z | ---
license: openrail
---
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|
open-llm-leaderboard/details_kyujinpy__PlatYi-34B-LoRA | open-llm-leaderboard | 2023-12-05T00:57:25Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T00:56:39Z | ---
pretty_name: Evaluation run of kyujinpy/PlatYi-34B-LoRA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [kyujinpy/PlatYi-34B-LoRA](https://huggingface.co/kyujinpy/PlatYi-34B-LoRA) 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_kyujinpy__PlatYi-34B-LoRA\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-05T00:53:53.251371](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-LoRA/blob/main/results_2023-12-05T00-53-53.251371.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\": 0.774827191806162,\n\
\ \"acc_stderr\": 0.027570240573755966,\n \"acc_norm\": 0.7838406442171528,\n\
\ \"acc_norm_stderr\": 0.028074245588194685,\n \"mc1\": 0.3733170134638923,\n\
\ \"mc1_stderr\": 0.01693237055757063,\n \"mc2\": 0.5332425206331443,\n\
\ \"mc2_stderr\": 0.014839740435159312\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620189,\n\
\ \"acc_norm\": 0.6715017064846417,\n \"acc_norm_stderr\": 0.013724978465537297\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6567416849233221,\n\
\ \"acc_stderr\": 0.00473826494473715,\n \"acc_norm\": 0.8537143995220076,\n\
\ \"acc_norm_stderr\": 0.003526700741879443\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7555555555555555,\n\
\ \"acc_stderr\": 0.03712537833614866,\n \"acc_norm\": 0.7555555555555555,\n\
\ \"acc_norm_stderr\": 0.03712537833614866\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8947368421052632,\n \"acc_stderr\": 0.024974533450920697,\n\
\ \"acc_norm\": 0.8947368421052632,\n \"acc_norm_stderr\": 0.024974533450920697\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.81,\n\
\ \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.81,\n \
\ \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02461829819586651,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02461829819586651\n },\n\
\ \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8958333333333334,\n\
\ \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n\
\ \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.7398843930635838,\n\
\ \"acc_stderr\": 0.03345036916788991,\n \"acc_norm\": 0.7398843930635838,\n\
\ \"acc_norm_stderr\": 0.03345036916788991\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5686274509803921,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.5686274509803921,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n\
\ \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7957446808510639,\n \"acc_stderr\": 0.026355158413349424,\n\
\ \"acc_norm\": 0.7957446808510639,\n \"acc_norm_stderr\": 0.026355158413349424\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5789473684210527,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.8137931034482758,\n \"acc_stderr\": 0.03243946159004616,\n\
\ \"acc_norm\": 0.8137931034482758,\n \"acc_norm_stderr\": 0.03243946159004616\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.7380952380952381,\n \"acc_stderr\": 0.02264421261552521,\n \"\
acc_norm\": 0.7380952380952381,\n \"acc_norm_stderr\": 0.02264421261552521\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5873015873015873,\n\
\ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.5873015873015873,\n\
\ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9161290322580645,\n\
\ \"acc_stderr\": 0.015769027496775667,\n \"acc_norm\": 0.9161290322580645,\n\
\ \"acc_norm_stderr\": 0.015769027496775667\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.6699507389162561,\n \"acc_stderr\": 0.033085304262282574,\n\
\ \"acc_norm\": 0.6699507389162561,\n \"acc_norm_stderr\": 0.033085304262282574\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \"acc_norm\"\
: 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706473,\n\
\ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706473\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\
acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\
\ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8256410256410256,\n \"acc_stderr\": 0.01923724980340523,\n \
\ \"acc_norm\": 0.8256410256410256,\n \"acc_norm_stderr\": 0.01923724980340523\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.4740740740740741,\n \"acc_stderr\": 0.03044452852881074,\n \
\ \"acc_norm\": 0.4740740740740741,\n \"acc_norm_stderr\": 0.03044452852881074\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8781512605042017,\n \"acc_stderr\": 0.021248144538412016,\n\
\ \"acc_norm\": 0.8781512605042017,\n \"acc_norm_stderr\": 0.021248144538412016\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5562913907284768,\n \"acc_stderr\": 0.04056527902281733,\n \"\
acc_norm\": 0.5562913907284768,\n \"acc_norm_stderr\": 0.04056527902281733\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9284403669724771,\n \"acc_stderr\": 0.011051255247815462,\n \"\
acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.011051255247815462\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.7175925925925926,\n \"acc_stderr\": 0.030701372111510927,\n \"\
acc_norm\": 0.7175925925925926,\n \"acc_norm_stderr\": 0.030701372111510927\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089678,\n \"\
acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089678\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.9282700421940928,\n \"acc_stderr\": 0.01679698961111959,\n \
\ \"acc_norm\": 0.9282700421940928,\n \"acc_norm_stderr\": 0.01679698961111959\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8116591928251121,\n\
\ \"acc_stderr\": 0.026241132996407256,\n \"acc_norm\": 0.8116591928251121,\n\
\ \"acc_norm_stderr\": 0.026241132996407256\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.02871877688934232,\n\
\ \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.02871877688934232\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540637,\n \"\
acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540637\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\
\ \"acc_stderr\": 0.029239272675632748,\n \"acc_norm\": 0.8981481481481481,\n\
\ \"acc_norm_stderr\": 0.029239272675632748\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8650306748466258,\n \"acc_stderr\": 0.02684576505455385,\n\
\ \"acc_norm\": 0.8650306748466258,\n \"acc_norm_stderr\": 0.02684576505455385\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6696428571428571,\n\
\ \"acc_stderr\": 0.044642857142857116,\n \"acc_norm\": 0.6696428571428571,\n\
\ \"acc_norm_stderr\": 0.044642857142857116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8932038834951457,\n \"acc_stderr\": 0.030581088928331356,\n\
\ \"acc_norm\": 0.8932038834951457,\n \"acc_norm_stderr\": 0.030581088928331356\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n\
\ \"acc_stderr\": 0.016534627684311357,\n \"acc_norm\": 0.9316239316239316,\n\
\ \"acc_norm_stderr\": 0.016534627684311357\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \
\ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9080459770114943,\n\
\ \"acc_stderr\": 0.010333225570778513,\n \"acc_norm\": 0.9080459770114943,\n\
\ \"acc_norm_stderr\": 0.010333225570778513\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.838150289017341,\n \"acc_stderr\": 0.019829299214925416,\n\
\ \"acc_norm\": 0.838150289017341,\n \"acc_norm_stderr\": 0.019829299214925416\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7687150837988826,\n\
\ \"acc_stderr\": 0.014102223623152586,\n \"acc_norm\": 0.7687150837988826,\n\
\ \"acc_norm_stderr\": 0.014102223623152586\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8725490196078431,\n \"acc_stderr\": 0.01909486481386516,\n\
\ \"acc_norm\": 0.8725490196078431,\n \"acc_norm_stderr\": 0.01909486481386516\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8392282958199357,\n\
\ \"acc_stderr\": 0.02086238808239191,\n \"acc_norm\": 0.8392282958199357,\n\
\ \"acc_norm_stderr\": 0.02086238808239191\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8796296296296297,\n \"acc_stderr\": 0.018105414094329676,\n\
\ \"acc_norm\": 0.8796296296296297,\n \"acc_norm_stderr\": 0.018105414094329676\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6808510638297872,\n \"acc_stderr\": 0.027807990141320186,\n \
\ \"acc_norm\": 0.6808510638297872,\n \"acc_norm_stderr\": 0.027807990141320186\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6232073011734028,\n\
\ \"acc_stderr\": 0.012376459593894405,\n \"acc_norm\": 0.6232073011734028,\n\
\ \"acc_norm_stderr\": 0.012376459593894405\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.022368672562886747,\n\
\ \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.022368672562886747\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.8464052287581699,\n \"acc_stderr\": 0.014586690876223224,\n \
\ \"acc_norm\": 0.8464052287581699,\n \"acc_norm_stderr\": 0.014586690876223224\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n\
\ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n\
\ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8367346938775511,\n \"acc_stderr\": 0.02366169917709861,\n\
\ \"acc_norm\": 0.8367346938775511,\n \"acc_norm_stderr\": 0.02366169917709861\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\
\ \"acc_stderr\": 0.02207632610182466,\n \"acc_norm\": 0.8905472636815921,\n\
\ \"acc_norm_stderr\": 0.02207632610182466\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n\
\ \"acc_stderr\": 0.03851597683718533,\n \"acc_norm\": 0.572289156626506,\n\
\ \"acc_norm_stderr\": 0.03851597683718533\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276908,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276908\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3733170134638923,\n\
\ \"mc1_stderr\": 0.01693237055757063,\n \"mc2\": 0.5332425206331443,\n\
\ \"mc2_stderr\": 0.014839740435159312\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8366219415943172,\n \"acc_stderr\": 0.010390695970273763\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.40636846095526913,\n \
\ \"acc_stderr\": 0.013528846685413242\n }\n}\n```"
repo_url: https://huggingface.co/kyujinpy/PlatYi-34B-LoRA
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|arc:challenge|25_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|gsm8k|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hellaswag|10_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T00-53-53.251371.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-05T00-53-53.251371.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- '**/details_harness|winogrande|5_2023-12-05T00-53-53.251371.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-05T00-53-53.251371.parquet'
- config_name: results
data_files:
- split: 2023_12_05T00_53_53.251371
path:
- results_2023-12-05T00-53-53.251371.parquet
- split: latest
path:
- results_2023-12-05T00-53-53.251371.parquet
---
# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-LoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/kyujinpy/PlatYi-34B-LoRA
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-LoRA](https://huggingface.co/kyujinpy/PlatYi-34B-LoRA) 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_kyujinpy__PlatYi-34B-LoRA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T00:53:53.251371](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-LoRA/blob/main/results_2023-12-05T00-53-53.251371.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": 0.774827191806162,
"acc_stderr": 0.027570240573755966,
"acc_norm": 0.7838406442171528,
"acc_norm_stderr": 0.028074245588194685,
"mc1": 0.3733170134638923,
"mc1_stderr": 0.01693237055757063,
"mc2": 0.5332425206331443,
"mc2_stderr": 0.014839740435159312
},
"harness|arc:challenge|25": {
"acc": 0.643344709897611,
"acc_stderr": 0.013998056902620189,
"acc_norm": 0.6715017064846417,
"acc_norm_stderr": 0.013724978465537297
},
"harness|hellaswag|10": {
"acc": 0.6567416849233221,
"acc_stderr": 0.00473826494473715,
"acc_norm": 0.8537143995220076,
"acc_norm_stderr": 0.003526700741879443
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7555555555555555,
"acc_stderr": 0.03712537833614866,
"acc_norm": 0.7555555555555555,
"acc_norm_stderr": 0.03712537833614866
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8947368421052632,
"acc_stderr": 0.024974533450920697,
"acc_norm": 0.8947368421052632,
"acc_norm_stderr": 0.024974533450920697
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.81,
"acc_stderr": 0.03942772444036623,
"acc_norm": 0.81,
"acc_norm_stderr": 0.03942772444036623
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8,
"acc_stderr": 0.02461829819586651,
"acc_norm": 0.8,
"acc_norm_stderr": 0.02461829819586651
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8958333333333334,
"acc_stderr": 0.025545239210256917,
"acc_norm": 0.8958333333333334,
"acc_norm_stderr": 0.025545239210256917
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.45,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7398843930635838,
"acc_stderr": 0.03345036916788991,
"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.03345036916788991
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5686274509803921,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.5686274509803921,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7957446808510639,
"acc_stderr": 0.026355158413349424,
"acc_norm": 0.7957446808510639,
"acc_norm_stderr": 0.026355158413349424
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5789473684210527,
"acc_stderr": 0.046446020912223177,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.046446020912223177
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.8137931034482758,
"acc_stderr": 0.03243946159004616,
"acc_norm": 0.8137931034482758,
"acc_norm_stderr": 0.03243946159004616
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.7380952380952381,
"acc_stderr": 0.02264421261552521,
"acc_norm": 0.7380952380952381,
"acc_norm_stderr": 0.02264421261552521
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5873015873015873,
"acc_stderr": 0.04403438954768176,
"acc_norm": 0.5873015873015873,
"acc_norm_stderr": 0.04403438954768176
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9161290322580645,
"acc_stderr": 0.015769027496775667,
"acc_norm": 0.9161290322580645,
"acc_norm_stderr": 0.015769027496775667
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6699507389162561,
"acc_stderr": 0.033085304262282574,
"acc_norm": 0.6699507389162561,
"acc_norm_stderr": 0.033085304262282574
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.87,
"acc_stderr": 0.03379976689896309,
"acc_norm": 0.87,
"acc_norm_stderr": 0.03379976689896309
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8666666666666667,
"acc_stderr": 0.026544435312706473,
"acc_norm": 0.8666666666666667,
"acc_norm_stderr": 0.026544435312706473
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9242424242424242,
"acc_stderr": 0.018852670234993093,
"acc_norm": 0.9242424242424242,
"acc_norm_stderr": 0.018852670234993093
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9689119170984456,
"acc_stderr": 0.012525310625527033,
"acc_norm": 0.9689119170984456,
"acc_norm_stderr": 0.012525310625527033
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8256410256410256,
"acc_stderr": 0.01923724980340523,
"acc_norm": 0.8256410256410256,
"acc_norm_stderr": 0.01923724980340523
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.4740740740740741,
"acc_stderr": 0.03044452852881074,
"acc_norm": 0.4740740740740741,
"acc_norm_stderr": 0.03044452852881074
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8781512605042017,
"acc_stderr": 0.021248144538412016,
"acc_norm": 0.8781512605042017,
"acc_norm_stderr": 0.021248144538412016
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.5562913907284768,
"acc_stderr": 0.04056527902281733,
"acc_norm": 0.5562913907284768,
"acc_norm_stderr": 0.04056527902281733
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9284403669724771,
"acc_stderr": 0.011051255247815462,
"acc_norm": 0.9284403669724771,
"acc_norm_stderr": 0.011051255247815462
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.7175925925925926,
"acc_stderr": 0.030701372111510927,
"acc_norm": 0.7175925925925926,
"acc_norm_stderr": 0.030701372111510927
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9264705882352942,
"acc_stderr": 0.018318855850089678,
"acc_norm": 0.9264705882352942,
"acc_norm_stderr": 0.018318855850089678
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.9282700421940928,
"acc_stderr": 0.01679698961111959,
"acc_norm": 0.9282700421940928,
"acc_norm_stderr": 0.01679698961111959
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8116591928251121,
"acc_stderr": 0.026241132996407256,
"acc_norm": 0.8116591928251121,
"acc_norm_stderr": 0.026241132996407256
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8778625954198473,
"acc_stderr": 0.02871877688934232,
"acc_norm": 0.8778625954198473,
"acc_norm_stderr": 0.02871877688934232
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8925619834710744,
"acc_stderr": 0.028268812192540637,
"acc_norm": 0.8925619834710744,
"acc_norm_stderr": 0.028268812192540637
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8981481481481481,
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```
### 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] | [
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|
mhzarem76/fintuned-llm2 | mhzarem76 | 2023-12-05T01:02:56Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T01:02:54Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 18997844
num_examples: 51942
download_size: 11986973
dataset_size: 18997844
---
# Dataset Card for "fintuned-llm2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
kfkas/test_DPO_dataset | kfkas | 2023-12-05T01:22:23Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T01:22:19Z | Invalid username or password. | [
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|
AdaptLLM/medicine-tasks | AdaptLLM | 2023-12-09T04:38:58Z | 23,366 | 3 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:conversational",
"language:en",
"biology",
"medical",
"arxiv:2309.09530",
"region:us"
]
| [
"text-classification",
"question-answering",
"zero-shot-classification",
"conversational"
]
| 2023-09-19T14:53:35Z | ---
configs:
- config_name: ChemProt
data_files:
- split: test
path: ChemProt/test.json
- config_name: MQP
data_files:
- split: test
path: MedQs/test.json
- config_name: PubMedQA
data_files:
- split: test
path: pubmed_qa/test.json
- config_name: RCT
data_files:
- split: test
path: RCT/test.json
- config_name: USMLE
data_files:
- split: test
path: usmle/test.json
task_categories:
- text-classification
- question-answering
- zero-shot-classification
- conversational
language:
- en
tags:
- biology
- medical
---
# Adapt (Large) Language Models to Domains
This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
**************************** **Updates** ****************************
* 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B.
* 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [base models](https://huggingface.co/AdaptLLM/finance-LLM) developed from LLaMA-1-7B.
## Domain-Specific LLaMA-1
In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
</p>
## Domain-Specific LLaMA-2-Chat
Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
For example, to chat with the biomedicine model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AdaptLLM/medicine-chat")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/medicine-chat", use_fast=False)
# Put your input here:
user_input = '''Question: Which of the following is an example of monosomy?
Options:
- 46,XX
- 47,XXX
- 69,XYY
- 45,X
Please provide your choice first and then provide explanations if possible.'''
# We use the prompt template of LLaMA-2-Chat demo
prompt = f"<s>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
```
## Domain-Specific Tasks
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
## Citation
If you find our work helpful, please cite us:
```bibtex
@article{adaptllm,
title = {Adapting Large Language Models via Reading Comprehension},
author = {Daixuan Cheng and Shaohan Huang and Furu Wei},
journal = {CoRR},
volume = {abs/2309.09530},
year = {2023}
}
``` | [
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language_bcp47:
- be-tarask
- en-simple
---
# Dataset Card for Wikimedia Wikipedia
## 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://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **Repository:**
- **Paper:**
- **Point of Contact:**
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each conatining a single train split.
Each example contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump, and you can load them per date and language this way:
```python
from datasets import load_dataset
ds = load_dataset("wikimedia/wikipedia", "20231101.en")
```
#### Data Visualization
Click the [Nomic Atlas](https://atlas.nomic.ai/map/475c26d7-b142-4795-9887-02b6eeb18dc0/0d312be6-a3bb-4586-b6b7-53dcd0cbefa5) map below to visualize the 6.4 million samples in the `20231101.en` split.
<a href="https://atlas.nomic.ai/map/475c26d7-b142-4795-9887-02b6eeb18dc0/0d312be6-a3bb-4586-b6b7-53dcd0cbefa5">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6480c476cacb1c4a0696eeb8/sZNN6Vubc0Oue83vKaJUu.webp" alt="Nomic-Atlas Wikipedia Map" width="25%"/>
</a>
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of languages here: https://meta.wikimedia.org/wiki/List_of_Wikipedias
## Dataset Structure
### Data Instances
An example looks as follows:
```
{'id': '1',
'url': 'https://simple.wikipedia.org/wiki/April',
'title': 'April',
'text': 'April is the fourth month...'
}
```
### Data Fields
The data fields are the same among all configurations:
- `id` (`str`): ID of the article.
- `url` (`str`): URL of the article.
- `title` (`str`): Title of the article.
- `text` (`str`): Text content of the article.
### Data Splits
All configurations contain a single `train` split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is built from the Wikipedia dumps: https://dumps.wikimedia.org
You can find the full list of languages and dates here: https://dumps.wikimedia.org/backup-index.html
The articles have been parsed using the [`mwparserfromhell`](https://mwparserfromhell.readthedocs.io) tool.
When uploading the data files for the 20231101 dump, we noticed that the Wikimedia Dumps website does not contain this date dump
for the "bbc", "dga", nor "zgh" Wikipedias. We have reported the issue to the Wikimedia Phabricator: https://phabricator.wikimedia.org/T351761
#### 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
Copyright licensing information: https://dumps.wikimedia.org/legal.html
All original textual content is licensed under the [GNU Free Documentation License](https://www.gnu.org/licenses/fdl-1.3.html) (GFDL)
and the [Creative Commons Attribution-Share-Alike 3.0 License](https://creativecommons.org/licenses/by-sa/3.0/).
Some text may be available only under the Creative Commons license; see their [Terms of Use](https://foundation.wikimedia.org/wiki/Policy:Terms_of_Use) for details.
Text written by some authors may be released under additional licenses or into the public domain.
### Citation Information
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
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|
philipphager/baidu-ultr_baidu-base-12L | philipphager | 2023-12-05T09:57:53Z | 5,690 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
]
| null | 2023-11-24T15:37:16Z | ---
license: cc-by-nc-4.0
---
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|
EuropeanParliament/Eurovoc | EuropeanParliament | 2023-12-07T08:25:00Z | 4,496 | 0 | [
"license:eupl-1.1",
"region:us"
]
| null | 2023-09-01T07:46:44Z | ---
license: eupl-1.1
configs:
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---
# 🇪🇺 🏷️ EuroVoc dataset
This dataset contains more that 3,700,000 documents in 39 languages with associated EuroVoc labels.
## What's Cellar ?
Cellar is the common data repository of the Publications Office of the European Union. Digital publications and metadata are stored in and disseminated via Cellar, in order to be used by humans and machines. Aiming to transparently serve users, Cellar stores multilingual publications and metadata, it is open to all EU citizens and provides machine-readable data.
https://op.europa.eu/fr/web/cellar
## Why was this dataset created ?
"Extreme classification come with challenges of scalability due to large label spaces, data sparsity issues due to insufficient training samples."
https://medium.com/datapy-ai/extreme-multi-label-classification-for-eurovoc-b51d74623820
## How was dataset this created ?
The source code is available, check `cellar.py`
## When this dataset was created ?
14 July 2023
## What are the main characteristics of this dataset ?
There are a total of 39 different languages present in this dataset, of which some are EU languages and some are not. As the following graph illustrates, most of the documents of the dataset are written in EU languages (English being the most present language in the dataset), and the non-EU languages are very poorly represented (for example Arabic, Japanese,...). Note that since the Irish language (`gle`) was granted full official and working status in the EU in 2022, there are very few documents in that language. Additionally, Croatian (`hrv`) is also less represented in the dataset as Croatia is the latest country to have joined the EU in 2013.
The lengths of the documents also varies depending on the language it is written in. The document lengths are quite variable, especially in English. There is therefore a quite large disparity in document lengths in this dataset. Note that this boxplot does not present the outliers, since the length of certain documents can contain up to 86 million characters. The red lines in the boxplot indicates the median length of the documents for each language.
We notice that the documents in Irish have a very wide variability in document lengths, due to the fact it has very few documents. Therefore, we present the same boxplot without the Irish language in order to visualize with more detail the document length distribution in the other languages.
## How is the data structured ?
An example of a sample of this dataset is the following :
```json
{
"title": "Commission information notice...",
"date": "2023-09-29",
"eurovoc_concepts": ["air transport", "intra-EU transport"],
"url": "http://publications.europa.eu/resource/cellar/ec99987f-5e69-11ee-9220-01aa75ed71a1",
"lang": "eng",
"formats": ["fmx4", "pdfa2a", "xhtml"],
"text": "To ensure ownership by the relevant actors,..."
}
```
- `title` : title of the document
- `date` : publication date of the document
- `eurovoc_concepts` : list of the EuroVoc concepts related to this document
- `url` : URL to access the document
- `formats` : list of formats in which the original document is available
- `text` : text content of the document
## Bibliography
- Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2019. Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 78–87, Minneapolis, Minnesota. Association for Computational Linguistics.
- I. Chalkidis, M. Fergadiotis, P. Malakasiotis and I. Androutsopoulos, Large-Scale Multi-Label Text Classification on EU Legislation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, (short papers), 2019.
- Andrei-Marius Avram, Vasile Pais, and Dan Ioan Tufis. 2021. PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 92–101, Held Online. INCOMA Ltd..
- SHAHEEN, Zein, WOHLGENANNT, Gerhard, et FILTZ, Erwin. Large scale legal text classification using transformer models. arXiv preprint arXiv:2010.12871, 2020.
## Author(s)
Sébastien Campion <[email protected]>
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|
Open-Orca/SlimOrca-Dedup | Open-Orca | 2023-12-08T03:38:07Z | 3,991 | 14 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"license:mit",
"code",
"art",
"music",
"legal",
"finance",
"biology",
"chemistry",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
]
| [
"text-classification",
"question-answering",
"text-generation"
]
| 2023-10-13T16:45:49Z | ---
license: mit
task_categories:
- text-classification
- question-answering
- text-generation
tags:
- code
- art
- music
- legal
- finance
- biology
- chemistry
pretty_name: SlimOrca Dedup
size_categories:
- 100K<n<1M
---
----
# Overview
----
"SlimOrca Dedup" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.
# Key Features
- Removal of RLHF instances.
- Deduplication using minhash and Jaccard similarity techniques.
# Demo Models
Note: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.
* https://huggingface.co/openaccess-ai-collective/jackalope-7b
* https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca
----
# Dataset format
----
**Basic Structure**
This dataset uses basic sharegpt formatting. Example and explanation of the schema is below:
```json
{
"conversations": [
{"from": "system", "value": "You are an AI assistant..."},
{"from": "human", "value": "Write an article based on this..."},
{"from": "gpt", "value": "Title: Tragedy Strikes in Sydney..."}
]
}
```
**Message Formatting**
- **"from"**: A string indicating the sender of the message. Possible senders are "system", "human", and "gpt".
- **"value"**: A string containing the message or instruction from the sender.
**Message roles**
- ** System: ** The system provides instructions or guidelines for the task to the large language model (LLM).
- ** Human: ** The human provides prompts or queries for the AI model to respond to.
- ** GPT: ** The language model, generates responses or content based on the prompts or queries provided by the human. messages from this role only ever follow messages from the human role.
----
# Citation
----
```bibtex
@misc{SlimOrcaDedup,
title = {SlimOrca Dedup: A Deduplicated Subset of SlimOrca},
author = {Wing Lian and Guan Wang and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium" and Nathan Hoos},
year = {2023},
publisher = {HuggingFace},
url = {https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup/}
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
``` | [
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]
|
AdaptLLM/law-tasks | AdaptLLM | 2023-12-09T04:38:10Z | 3,513 | 5 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:conversational",
"language:en",
"legal",
"arxiv:2309.09530",
"region:us"
]
| [
"text-classification",
"question-answering",
"zero-shot-classification",
"conversational"
]
| 2023-09-19T07:44:48Z | ---
configs:
- config_name: SCOTUS
data_files:
- split: test
path: scotus/test.json
- config_name: CaseHOLD
data_files:
- split: test
path: case_hold/test.json
- config_name: UNFAIR_ToS
data_files:
- split: test
path: unfair_tos/test.json
task_categories:
- text-classification
- question-answering
- zero-shot-classification
- conversational
language:
- en
tags:
- legal
---
# Adapt (Large) Language Models to Domains
This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
**************************** **Updates** ****************************
* 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B.
* 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [base models](https://huggingface.co/AdaptLLM/finance-LLM) developed from LLaMA-1-7B.
## Domain-Specific LLaMA-1
In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
</p>
## Domain-Specific LLaMA-2-Chat
Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
For example, to chat with the law model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AdaptLLM/law-chat")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/law-chat", use_fast=False)
# Put your input here:
user_input = '''Question: Which of the following is false about ex post facto laws?
Options:
- They make criminal an act that was innocent when committed.
- They prescribe greater punishment for an act than was prescribed when it was done.
- They increase the evidence required to convict a person than when the act was done.
- They alter criminal offenses or punishment in a substantially prejudicial manner for the purpose of punishing a person for some past activity.
Please provide your choice first and then provide explanations if possible.'''
# We use the prompt template of LLaMA-2-Chat demo
prompt = f"<s>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
```
## Domain-Specific Tasks
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
## Citation
If you find our work helpful, please cite us:
```bibtex
@article{adaptllm,
title = {Adapting Large Language Models via Reading Comprehension},
author = {Daixuan Cheng and Shaohan Huang and Furu Wei},
journal = {CoRR},
volume = {abs/2309.09530},
year = {2023}
}
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|
AI4Math/MathVista | AI4Math | 2023-12-09T08:32:58Z | 3,347 | 42 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:visual-question-answering",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"task_ids:visual-question-answering",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:zh",
"language:fa",
"license:cc-by-sa-4.0",
"multi-modal-qa",
"math-qa",
"figure-qa",
"geometry-qa",
"math-word-problem",
"textbook-qa",
"vqa",
"arithmetic-reasoning",
"statistical-reasoning",
"algebraic-reasoning",
"geometry-reasoning",
"numeric-common-sense",
"scientific-reasoning",
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"geometry-diagram",
"synthetic-scene",
"chart",
"plot",
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"table",
"function-plot",
"abstract-scene",
"puzzle-test",
"document-image",
"medical-image",
"mathematics",
"science",
"chemistry",
"biology",
"physics",
"engineering",
"natural-science",
"arxiv:2310.02255",
"region:us"
]
| [
"multiple-choice",
"question-answering",
"visual-question-answering",
"text-classification"
]
| 2023-10-15T17:49:10Z | ---
annotations_creators:
- expert-generated
- found
language_creators:
- expert-generated
- found
language:
- en
- zh
- fa
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
- text-classification
task_ids:
- multiple-choice-qa
- closed-domain-qa
- open-domain-qa
- visual-question-answering
- multi-class-classification
paperswithcode_id: mathvista
pretty_name: MathVista
tags:
- multi-modal-qa
- math-qa
- figure-qa
- geometry-qa
- math-word-problem
- textbook-qa
- vqa
- arithmetic-reasoning
- statistical-reasoning
- algebraic-reasoning
- geometry-reasoning
- numeric-common-sense
- scientific-reasoning
- logical-reasoning
- geometry-diagram
- synthetic-scene
- chart
- plot
- scientific-figure
- table
- function-plot
- abstract-scene
- puzzle-test
- document-image
- medical-image
- mathematics
- science
- chemistry
- biology
- physics
- engineering
- natural-science
configs:
- config_name: default
data_files:
- split: testmini
path: data/testmini-*
- split: test
path: data/test-*
dataset_info:
features:
- name: pid
dtype: string
- name: question
dtype: string
- name: image
dtype: string
- name: decoded_image
dtype: image
- name: choices
sequence: string
- name: unit
dtype: string
- name: precision
dtype: float64
- name: answer
dtype: string
- name: question_type
dtype: string
- name: answer_type
dtype: string
- name: metadata
struct:
- name: category
dtype: string
- name: context
dtype: string
- name: grade
dtype: string
- name: img_height
dtype: int64
- name: img_width
dtype: int64
- name: language
dtype: string
- name: skills
sequence: string
- name: source
dtype: string
- name: split
dtype: string
- name: task
dtype: string
- name: query
dtype: string
splits:
- name: testmini
num_bytes: 142635198.0
num_examples: 1000
- name: test
num_bytes: 648291350.22
num_examples: 5141
download_size: 885819490
dataset_size: 790926548.22
---
# Dataset Card for MathVista
- [Dataset Description](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-description)
- [Paper Information](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#paper-information)
- [Dataset Examples](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-examples)
- [Leaderboard](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#leaderboard)
- [Dataset Usage](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-usage)
- [Data Downloading](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-downloading)
- [Data Format](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-format)
- [Data Visualization](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-visualization)
- [Data Source](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-source)
- [Automatic Evaluation](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#automatic-evaluation)
- [License](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#license)
- [Citation](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#citation)
## Dataset Description
**MathVista** is a consolidated Mathematical reasoning benchmark within Visual contexts. It consists of **three newly created datasets, IQTest, FunctionQA, and PaperQA**, which address the missing visual domains and are tailored to evaluate logical reasoning on puzzle test figures, algebraic reasoning over functional plots, and scientific reasoning with academic paper figures, respectively. It also incorporates **9 MathQA datasets** and **19 VQA datasets** from the literature, which significantly enrich the diversity and complexity of visual perception and mathematical reasoning challenges within our benchmark. In total, **MathVista** includes **6,141 examples** collected from **31 different datasets**.
## Paper Information
- Paper: https://arxiv.org/abs/2310.02255
- Code: https://github.com/lupantech/MathVista
- Project: https://mathvista.github.io/
- Visualization: https://mathvista.github.io/#visualization
- Leaderboard: https://mathvista.github.io/#leaderboard
## Dataset Examples
Examples of our newly annotated datasets: IQTest, FunctionQA, and PaperQA:
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/our_new_3_datasets.png" style="zoom:40%;" />
<details>
<summary>🔍 Click to expand/collapse more examples</summary>
Examples of seven mathematical reasoning skills:
1. Arithmetic Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/ari.png" style="zoom:40%;" />
2. Statistical Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/sta.png" style="zoom:40%;" />
3. Algebraic Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/alg.png" style="zoom:40%;" />
4. Geometry Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/geo.png" style="zoom:40%;" />
5. Numeric common sense
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/num.png" style="zoom:40%;" />
6. Scientific Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/sci.png" style="zoom:40%;" />
7. Logical Reasoning
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/log.png" style="zoom:40%;" />
</details>
## Leaderboard
🏆 The leaderboard for the *testmini* set (1,000 examples) is available [here](https://mathvista.github.io/#leaderboard).
🏆 The leaderboard for the *test* set (5,141 examples) and the automatic evaluation on [CodaLab](https://codalab.org/) are under construction.
## Dataset Usage
### Data Downloading
All the data examples were divided into two subsets: *testmini* and *test*.
- **testmini**: 1,000 examples used for model development, validation, or for those with limited computing resources.
- **test**: 5,141 examples for standard evaluation. Notably, the answer labels for test will NOT be publicly released.
You can download this dataset by the following command (make sure that you have installed [Huggingface Datasets](https://huggingface.co/docs/datasets/quickstart)):
```python
from datasets import load_dataset
dataset = load_dataset("AI4Math/MathVista")
```
Here are some examples of how to access the downloaded dataset:
```python
# print the first example on the testmini set
print(dataset["testmini"][0])
print(dataset["testmini"][0]['pid']) # print the problem id
print(dataset["testmini"][0]['question']) # print the question text
print(dataset["testmini"][0]['query']) # print the query text
print(dataset["testmini"][0]['image']) # print the image path
print(dataset["testmini"][0]['answer']) # print the answer
dataset["testmini"][0]['decoded_image'] # display the image
# print the first example on the test set
print(dataset["test"][0])
```
### Data Format
The dataset is provided in json format and contains the following attributes:
```json
{
"question": [string] The question text,
"image": [string] A file path pointing to the associated image,
"choices": [list] Choice options for multiple-choice problems. For free-form problems, this could be a 'none' value,
"unit": [string] The unit associated with the answer, e.g., "m^2", "years". If no unit is relevant, it can be a 'none' value,
"precision": [integer] The number of decimal places the answer should be rounded to,
"answer": [string] The correct answer for the problem,
"question_type": [string] The type of question: "multi_choice" or "free_form",
"answer_type": [string] The format of the answer: "text", "integer", "float", or "list",
"pid": [string] Problem ID, e.g., "1",
"metadata": {
"split": [string] Data split: "testmini" or "test",
"language": [string] Question language: "English", "Chinese", or "Persian",
"img_width": [integer] The width of the associated image in pixels,
"img_height": [integer] The height of the associated image in pixels,
"source": [string] The source dataset from which the problem was taken,
"category": [string] The category of the problem: "math-targeted-vqa" or "general-vqa",
"task": [string] The task of the problem, e.g., "geometry problem solving",
"context": [string] The visual context type of the associated image,
"grade": [string] The grade level of the problem, e.g., "high school",
"skills": [list] A list of mathematical reasoning skills that the problem tests
},
"query": [string] the query text used as input (prompt) for the evaluation model
}
```
### Data Visualization
🎰 You can explore the dataset in an interactive way [here](https://mathvista.github.io/#visualization).
<details>
<summary>Click to expand/collapse the visualization page screeshot.</summary>
<img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/data_visualizer.png" style="zoom:40%;" />
</details>
### Data Source
The **MathVista** dataset is derived from three newly collected datasets: IQTest, FunctionQA, and Paper, as well as 28 other source datasets. Details can be found in the [source.json](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/source.json) file. All these source datasets have been preprocessed and labeled for evaluation purposes.
### Automatic Evaluation
🔔 To automatically evaluate a model on the dataset, please refer to our GitHub repository [here](https://github.com/lupantech/MathVista/tree/main).
## License
The new contributions to our dataset are distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license, including
- The creation of three dataset: IQTest, FunctionQA, and Paper;
- The filtering and cleaning of source datasets;
- The standard formalization of instances for evaluation purposes;
- The annotations of metadata.
The copyright of the images and the questions belongs to the original authors, and the source of every image and original question can be found in the `metadata` field and in the [source.json](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/source.json) file. Alongside this license, the following conditions apply:
- **Purpose:** The dataset was primarily designed for use as a test set.
- **Commercial Use:** The dataset can be used commercially as a test set, but using it as a training set is prohibited. By accessing or using this dataset, you acknowledge and agree to abide by these terms in conjunction with the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license.
## Citation
If you use the **MathVista** dataset in your work, please kindly cite the paper using this BibTeX:
```
@article{lu2023mathvista,
title={MathVista: Evaluating Math Reasoning in Visual Contexts with GPT-4V, Bard, and Other Large Multimodal Models},
author={Lu, Pan and Bansal, Hritik and Xia, Tony and Liu, Jiacheng and Li, Chunyuan and Hajishirzi, Hannaneh and Cheng, Hao and Chang, Kai-Wei and Galley, Michel and Gao, Jianfeng},
journal={arXiv preprint arXiv:2310.02255},
year={2023}
}
``` | [
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|
AdaptLLM/finance-tasks | AdaptLLM | 2023-12-09T04:37:38Z | 3,103 | 7 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:conversational",
"language:en",
"finance",
"arxiv:2309.09530",
"region:us"
]
| [
"text-classification",
"question-answering",
"zero-shot-classification",
"conversational"
]
| 2023-09-19T03:17:07Z | ---
configs:
- config_name: ConvFinQA
data_files:
- split: test
path: ConviFinQA/test.json
- config_name: FiQA_SA
data_files:
- split: test
path: FiQA_SA/test.json
- config_name: FPB
data_files:
- split: test
path: FPB/test.json
- config_name: Headline
data_files:
- split: test
path: Headline/test.json
- config_name: NER
data_files:
- split: test
path: NER/test.json
task_categories:
- text-classification
- question-answering
- zero-shot-classification
- conversational
language:
- en
tags:
- finance
---
# Adapt (Large) Language Models to Domains
This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
**************************** **Updates** ****************************
* 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B.
* 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [base models](https://huggingface.co/AdaptLLM/finance-LLM) developed from LLaMA-1-7B.
## Domain-Specific LLaMA-1
In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
</p>
## Domain-Specific LLaMA-2-Chat
Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
For example, to chat with the finance model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat", use_fast=False)
# Put your input here:
user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered
Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange
MMM Chicago Stock Exchange, Inc.
1.500% Notes due 2026 MMM26 New York Stock Exchange
1.750% Notes due 2030 MMM30 New York Stock Exchange
1.500% Notes due 2031 MMM31 New York Stock Exchange
Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?'''
# We use the prompt template of LLaMA-2-Chat demo
prompt = f"<s>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
```
## Domain-Specific Tasks
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
## Citation
If you find our work helpful, please cite us:
```bibtex
@article{adaptllm,
title = {Adapting Large Language Models via Reading Comprehension},
author = {Daixuan Cheng and Shaohan Huang and Furu Wei},
journal = {CoRR},
volume = {abs/2309.09530},
year = {2023}
}
``` | [
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|
anyspeech/frame_labels | anyspeech | 2023-12-08T19:41:52Z | 1,083 | 0 | [
"region:us"
]
| null | 2023-12-08T05:32:05Z | ---
dataset_info:
features:
- name: converted_phonetic_detail
struct:
- name: start
sequence: float64
- name: stop
sequence: float64
- name: utterance
sequence: string
- name: dialect_region
dtype: string
- name: file
dtype: string
- name: frame_labels
sequence: string
- name: id
dtype: string
- name: merge_phonetic_detail
struct:
- name: start
sequence: float64
- name: stop
sequence: float64
- name: utterance
sequence: string
- name: phonetic_detail
sequence:
- name: start
dtype: int64
- name: stop
dtype: int64
- name: utterance
dtype: string
- name: sentence_type
dtype: string
- name: speaker_id
dtype: string
- name: text
dtype: string
- name: word_detail
struct:
- name: start
sequence: float64
- name: stop
sequence: float64
- name: utterance
sequence: string
- name: frame_labels_10ms
sequence: string
- name: audio
struct:
- name: array
sequence: float64
- name: sampling_rate
dtype: int64
splits:
- name: train
num_bytes: 1843096543
num_examples: 4620
- name: test
num_bytes: 673493381
num_examples: 1680
download_size: 558422047
dataset_size: 2516589924
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
DFKI-SLT/argmicro | DFKI-SLT | 2023-12-07T15:18:14Z | 1,076 | 0 | [
"size_categories:n<1K",
"language:en",
"language:de",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-08-08T16:17:53Z | ---
license: cc-by-nc-sa-4.0
language:
- en
- de
pretty_name: argmicro
size_categories:
- n<1K
---
# Dataset Card for "ArgMicro"
### Dataset Summary
The arg-microtexts corpus features 112 short argumentative texts. All texts were originally written in German and have been professionally translated to English.
Based on Freeman’s theory of the macro-structure of arguments ([1991](https://api.pageplace.de/preview/DT0400.9783110875843_A19822678/preview-9783110875843_A19822678.pdf); [2011](https://link.springer.com/book/10.1007/978-94-007-0357-5)) and Toulmin ([2003](https://www.cambridge.org/core/books/uses-of-argument/26CF801BC12004587B66778297D5567C))'s diagramming techniques, ArgMicro consists of `pro` (proponent) and `opp` (opponent) components and six types of relations: `seg` (segment), `add` (addition), `exa` (example), `reb` (rebut), `sup` (support), and `und` (undercut). It also introduced segment-based spans, which also contain non-argumentative parts, in order to cover the whole text.
### Supported Tasks and Leaderboards
- **Tasks:** Structure Prediction, Relation Identification, Central Claim Identification, Role Classification, Function Classification
- **Leaderboards:** \[More Information Needed\]
### Languages
German, with English translation (by a professional translator).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 2.89 MB
```
{
"id": "micro_b001",
"topic_id": "waste_separation",
"stance": 1,
"text": "Yes, it's annoying and cumbersome to separate your rubbish properly all the time. Three different bin bags stink away in the kitchen and have to be sorted into different wheelie bins. But still Germany produces way too much rubbish and too many resources are lost when what actually should be separated and recycled is burnt. We Berliners should take the chance and become pioneers in waste separation!",
"edus": {
"id": ["e1", "e2", "e3", "e4", "e5"],
"start": [0, 82, 184, 232, 326],
"end": [81, 183, 231, 325, 402]
},
"adus": {
"id": ["a1", "a2", "a3", "a4", "a5"],
"type": [0, 0, 1, 1, 1]
},
"edges": {
"id": ["c1", "c10", "c2", "c3", "c4", "c6", "c7", "c8", "c9"],
"src": ["a1", "e5", "a2", "a3", "a4", "e1", "e2", "e3", "e4"],
"trg": ["a5", "a5", "a1", "c1", "c3", "a1", "a2", "a3", "a4"],
"type": [4, 0, 1, 5, 3, 0, 0, 0, 0]
}
}
```
### Data Fields
- `id`: the instance `id` of the document, a `string` feature
- `topic_id`: the topic of the document, a `string` feature (see [list of topics](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/topics_triggers.md))
- `stance`: the index of stance on the topic, an `int` feature (see [stance labels](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L35))
- `text`: the text content of the document, a `string` feature
- `edus`: elementary discourse units; a segmented span of text (see the authors' further [explanation](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd#L17-L20))
- `id`: the instance `id` of EDUs, a list of `string` feature
- `start`: the indices indicating the inclusive start of the spans, a list of `int` feature
- `end`: the indices indicating the exclusive end of the spans, a list of `int` feature
- `adus`: argumentative discourse units; argumentatively relevant claims built on EDUs (see the authors' further [explanation](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd#L22-L28))
- `id`: the instance `id` of ADUs, a list of `string` feature
- `type`: the indices indicating the ADU type, a list of `int` feature (see [type list](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L36))
- `edges`: the relations between `adus` or `adus` and other `edges` (see the authors' further [explanation](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd#L39-L47))
- `id`: the instance `id` of edges, a list of `string` feature
- `src`: the `id` of `adus` indicating the source element in a relation, a list of `string` feature
- `trg`: the `id` of `adus` or `edges` indicating the target element in a relation, a list of `string` feature
- `type`: the indices indicating the edge type, a list of `int` feature (see [type list](https://huggingface.co/datasets/DFKI-SLT/argmicro/blob/main/argmicro.py#L37))
### Data Splits
| | train |
| -------------------------------------- | ----: |
| No. of instances | 112 |
| No. of sentences/instance (on average) | 5.1 |
### Data Labels
#### Stance
| Stance | Count | Percentage |
| ----------- | ----: | ---------: |
| `pro` | 46 | 41.1 % |
| `con` | 42 | 37.5 % |
| `unclear` | 1 | 0.9 % |
| `UNDEFINED` | 23 | 20.5 % |
- `pro`: yes, in favour of the proposed issue
- `con`: no, against the proposed issue
- `unclear`: the position of the author is unclear
- `UNDEFINED`: no stance label assigned
See [stances types](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd#L74-L83).
#### ADUs
| ADUs | Count | Percentage |
| ----- | ----: | ---------: |
| `pro` | 451 | 78.3 % |
| `opp` | 125 | 21.7 % |
- `pro`: proponent, who presents and defends his claims
- `opp`: opponent, who critically questions the proponent in a regimented fashion (Peldszus, 2015, p.5)
#### Relations
| Relations | Count | Percentage |
| -------------- | ----: | ---------: |
| support: `sup` | 281 | 55.2 % |
| support: `exa` | 9 | 1.8 % |
| attack: `und` | 65 | 12.8 % |
| attack: `reb` | 110 | 21.6 % |
| other: `joint` | 44 | 8.6 % |
- `sup`: support (ADU->ADU)
- `exa`: support by example (ADU->ADU)
- `add`: additional source, for combined/convergent arguments with multiple premises, i.e., linked support, convergent support, serial support (ADU->ADU)
- `reb`: rebutting attack (ADU->ADU)
- definition: "targeting another node and thereby challenging its acceptability"
- `und`: undercutting attack (ADU->Edge)
- definition: "targeting an edge and thereby challenging the acceptability of the inference from the source to the target node"
([P&S, 2016](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd); [EN annotation guideline](https://www.ling.uni-potsdam.de/~stede/Papers/ArgGuidelinesEnglish.pdf))
- `joint`: combines text segments if one does not express a complete proposition on its own, or if the author divides a clause/sentence into parts, using punctuation
See other corpus statistics in Peldszus (2015), Section 5.
## Dataset Creation
This section is composed of information and excerpts provided in Peldszus ([2015](https://peldszus.github.io/files/eca2015-preprint.pdf)).
### Curation Rationale
"Argumentation can, for theoretical purposes, be studied on the basis of carefully constructed examples that illustrate specific phenomena...\[We\] address this need by making a resource publicly available that is designed to fill a particular gap." (pp. 2-3)
### Source Data
23 texts were written by the authors as a “proof of concept” for the idea. These texts also have been used as examples in teaching and testing argumentation analysis with students.
90 texts have been collected in a controlled text generation experiment, where normal competent language users wrote short texts of controlled linguistic and rhetoric complexity.
#### Initial Data Collection and Normalization
"Our contribution is a collection of 112 “microtexts” that have been written in response to trigger questions, mostly in the form of “Should one do X”. The texts are short but at the same time “complete” in that they provide a standpoint and a justification, by necessity in a fairly dense form." (p.2)
"The probands were asked to first gather a list with the pros and cons of the trigger question, then take stance for one side and argue for it on the basis of their reflection in a short argumentative text. Each text was to fulfill three requirements: It should be about five segments long; all segments should be argumentatively relevant, either formulating the main claim of the text, supporting the main claim or another segment, or attacking the main claim or another segment. Also, the probands were asked that at least one possible objection to the claim should be considered in the text. Finally, the text should be written in such a way that it would be understandable without having its trigger question as a headline." (p.3)
"\[A\]ll texts have been corrected for spelling and grammar errors...Their segmentation was corrected when necessary...some modifications in the remaining segments to maintain text coherence, which we made as minimal as possible." (p.4)
"We thus constrained the translation to preserve the segmentation of the text on the one hand (effectively ruling out phrasal translations of clause-type segments) and to preserve its linearization on the other hand (disallowing changes to the order of appearance of arguments)." (p.5)
#### Who are the source language producers?
The texts with ids b001-b064 and k001-k031 have been collected in a controlled text generation experiment from 23 subjects discussing various controversial issues from a fixed list. All probands were native speakers of
German, of varying age, education and profession.
The texts with ids d01-d23 have been written by Andreas Peldszus, the author.
### Annotations
#### Annotation process
All texts are annotated with argumentation structures, following the scheme proposed in Peldszus & Stede ([2013](https://www.ling.uni-potsdam.de/~peldszus/ijcini2013-preprint.pdf)). For inter-annotator-agreement scores see Peldszus (2014). The (German) annotation guidelines are published in Peldszus, Warzecha, Stede (2016). See the annotation guidelines ([de](https://www.ling.uni-potsdam.de/~stede/Papers/ArgGuidelinesGerman.pdf), [en](https://www.ling.uni-potsdam.de/~stede/Papers/ArgGuidelinesEnglish.pdf)), and the [annotation schemes](https://github.com/peldszus/arg-microtexts/blob/master/corpus/arggraph.dtd).
"\[T\]he markup of argumentation structures in the full corpus was done by one expert annotator. All annotations have been checked, controversial instances have been discussed in a reconciliation phase by two or more expert annotators...The annotation of the corpus was originally done manually on paper. In follow-up annotations, we used GraPAT ([Sonntag & Stede, 2014](http://www.lrec-conf.org/proceedings/lrec2014/pdf/824_Paper.pdf))." (p.7)
#### Who are the annotators?
\[More Information Needed\]
### Personal and Sensitive Information
\[More Information Needed\]
## Considerations for Using the Data
### Social Impact of Dataset
"Automatic argumentation recognition has many possible applications, including improving document summarization (Teufel and Moens, 2002), retrieval capabilities of legal databases (Palau and Moens, 2011), opinion mining for commercial purposes, or also as a tool for assessing public
opinion on political questions.
"...\[W\]e suggest there is yet one resource missing that could facilitate the development of automatic argumentation recognition systems: Short texts with explicit argumentation, little argumentatively irrelevant material, less rhetorical gimmicks (or even deception), in clean written language."
(Peldszus, [2014](https://aclanthology.org/W14-2112.pdf), p. 88)
### Discussion of Biases
\[More Information Needed\]
### Other Known Limitations
\[More Information Needed\]
## Additional Information
### Dataset Curators
\[More Information Needed\]
### Licensing Information
The arg-microtexts corpus is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. (see [license agreement](https://creativecommons.org/licenses/by-nc-sa/4.0/))
### Citation Information
```
@inproceedings{peldszus2015annotated,
title={An annotated corpus of argumentative microtexts},
author={Peldszus, Andreas and Stede, Manfred},
booktitle={Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon},
volume={2},
pages={801--815},
year={2015}
}
```
```
@inproceedings{peldszus2014towards,
title={Towards segment-based recognition of argumentation structure in short texts},
author={Peldszus, Andreas},
booktitle={Proceedings of the First Workshop on Argumentation Mining},
pages={88--97},
year={2014}
}
```
### Contributions
Thanks to [@idalr](https://github.com/idalr) for adding this dataset. | [
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|
beanham/medsum | beanham | 2023-12-08T03:29:30Z | 990 | 0 | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"medical",
"region:us"
]
| [
"summarization"
]
| 2023-11-15T20:27:01Z | ---
task_categories:
- summarization
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
---
This dataset comes from the EACL 2023 paper: An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters
https://github.com/abachaa/MTS-Dialog/tree/main/Main-Dataset | [
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|
cyberagent/crello | cyberagent | 2023-12-05T14:07:33Z | 828 | 16 | [
"task_categories:unconditional-image-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cdla-permissive-2.0",
"graphic design",
"design templates",
"arxiv:2108.01249",
"region:us"
]
| [
"unconditional-image-generation"
]
| 2023-02-03T01:31:45Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cdla-permissive-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- unconditional-image-generation
task_ids: []
pretty_name: crello
tags:
- graphic design
- design templates
dataset_info:
features:
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dtype: string
- name: length
dtype: int64
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dtype:
class_label:
names:
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'1': HC
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'3': SMA
'4': EO
'5': BG
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dtype:
class_label:
names:
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'3': Facebook cover
'4': Twitter
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'8': Tumblr
'9': Image
'10': Pinterest
'11': Flayer
'12': FB event cover
'13': Postcard
'14': Invitation
'15': Youtube
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'17': Medium Rectangle
'18': Poster US
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'20': Large Rectangle
'21': Card
'22': Logo
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'25': Leaderboard
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'28': VK Universal Post
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'31': Book Cover
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'33': VK Community Cover
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'35': Zoom Background
'36': VK Post with Button
'37': T-Shirt
'38': Instagram Highlight Cover
'39': Coupon
'40': Letterhead
'41': IGTV Cover
'42': Schedule Planner
'43': Album Cover
'44': LinkedIn Cover
'45': Storyboard
'46': Recipe Card
'47': Invoice
'48': Resume
'49': Menu
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'51': Mind Map
'52': Label
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'57': Snapchat Geofilter
'58': Snapchat Moment Filter
'59': Twitch Offline Banner
'60': Twitch Profile Banner
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'62': Mobile Presentation
'63': Photo Book
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'65': Gallery Image
'66': Calendar
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dtype:
class_label:
names:
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'1': foodDrinks
'2': fashionStyle
'3': businessFinance
'4': homeStuff
'5': handcraftArt
'6': beauty
'7': leisureEntertainment
'8': natureWildlife
'9': educationScience
'10': technology
'11': medical
'12': socialActivityCharity
'13': realEstateBuilding
'14': sportExtreme
'15': travelsVacations
'16': pets
'17': religions
'18': citiesPlaces
'19': industry
'20': transportation
'21': kidsParents
'22': all
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dtype: string
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sequence:
class_label:
names:
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sequence: float32
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sequence: float32
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sequence: float32
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sequence:
sequence: float32
length: 3
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sequence: image
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sequence: string
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sequence:
class_label:
names:
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'2': Bebas Neue
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'4': Josefin Sans
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'7': Oswald
'8': Blogger
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'12': Rubik
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'15': Libre Baskerville
'16': Quicksand
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'18': Podkova
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'23': Playlist Script
'24': Arapey
'25': Baloo Tamma
'26': Graduate
'27': Titillium Web
'28': Kreon
'29': Nunito
'30': Rammetto One
'31': Anton
'32': Poiret One
'33': Alfa Slab One
'34': Righteous
'35': Play
'36': Space Mono
'37': Frank Ruhl Libre
'38': Yanone Kaffeesatz
'39': Pacifico
'40': Bangers
'41': Yellowtail
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'44': Merriweather
'45': Miriam Libre
'46': Crete Round
'47': Rubik One
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'98': Cabin Sketch
'99': Vollkorn
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'102': Satisfy
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'105': Glass Antiqua
'106': Mr Dafoe
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'135': Nothing You Could Do
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'137': Fredericka The Great
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'141': Dawning Of A New Day
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'143': Neucha
'144': Source Serif Pro
'145': Shadows Into Light Two
'146': Armata
'147': Cutive Mono
'148': Merienda One
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'150': Stalemate
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'153': Breathe Press
'154': Suez One
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'167': Julius Sans One
'168': Quattrocento
'169': V T323
'170': Finger Paint
'171': La Belle Aurore
'172': Inconsolata
'173': Press Start 2P
'174': Junge
'175': Iceberg
'176': Kelly Slab
'177': Handlee
'178': Rosario
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'180': Homemade Apple
'181': Londrina Shadow
'182': Meddon
'183': Elsie Swash Caps
'184': Share Tech Mono
'185': Black Ops One
'186': Fauna One
'187': Alice
'188': Arizonia
'189': Text Me One
'190': Nova Square
'191': Bungee Shade
'192': Just Me Again Down Here
'193': Jacques Francois Shadow
'194': Cousine
'195': Forum
'196': Architects Daughter
'197': Cedarville Cursive
'198': Elsie
'199': Sirin Stencil
'200': Vampiro One
'201': Dorsa
'202': Marcellus Sc
'203': Kumar One
'204': Allerta Stencil
'205': Courgette
'206': Rationale
'207': Gluk Znikomitno25
'208': Happy Monkey
'209': Stint Ultra Expanded
'210': Rock Salt
'211': Im Fell Dw Pica Sc
'212': Faster One
'213': Bellefair
'214': Wire One
'215': Geo
'216': Farsan
'217': League Script
'218': Chathura
'219': Euphoria Script
'220': Zeyada
'221': Jura
'222': Loved By The King
'223': Give You Glory
'224': Znikomitno24
'225': Gluk Glametrix
'226': Alegreya Sans
'227': Kristi
'228': Knewave Outline
'229': Pangolin
'230': Okolaks
'231': Seymour One
'232': Didact Gothic
'233': Kavivanar
'234': Underdog
'235': Alef
'236': Italianno
'237': Londrina Sketch
'238': Secular One
'239': Katibeh
'240': Caesar Dressing
'241': Lovers Quarrel
'242': Iceland
'243': Im Fell
'244': Waiting For The Sunrise
'245': David Libre
'246': Marck Script
'247': Kumar One Outline
'248': Znikomit
'249': Monsieur La Doulaise
'250': Gruppo
'251': Monofett
'252': Gfs Didot
'253': Petit Formal Script
'254': Dukomdesign Constantine
'255': Brusher
'256': Eb Garamond
'257': Ewert
'258': Bilbo
'259': Raleway Dots
'260': Gabriela
'261': Ruslan Display
- name: font_size
sequence: float32
- name: text_align
sequence:
class_label:
names:
'0': ''
'1': left
'2': center
'3': right
- name: angle
sequence: float32
- name: capitalize
sequence:
class_label:
names:
'0': 'false'
'1': 'true'
- name: line_height
sequence: float32
- name: letter_spacing
sequence: float32
- name: suitability
sequence:
class_label:
names:
'0': mobile
- name: keywords
sequence: string
- name: industries
sequence:
class_label:
names:
'0': marketingAds
'1': entertainmentLeisure
'2': services
'3': retail
'4': businessFinance
'5': educationTraining
'6': foodBeverages
'7': artCrafts
'8': fashionStyle
'9': healthWellness
'10': ecologyNature
'11': nonProfitCharity
'12': techGadgets
'13': beautyCosmetics
'14': homeLiving
'15': familyKids
'16': travelTourism
'17': sportFitness
'18': corporate
'19': petsAnimals
'20': realEstateConstruction
'21': transportDelivery
'22': religionFaith
'23': hrRecruitment
- name: preview
dtype: image
- name: cluster_index
dtype: int64
splits:
- name: train
num_bytes: 5058614277.34
num_examples: 19095
- name: validation
num_bytes: 538185754.149
num_examples: 1951
- name: test
num_bytes: 649876234.375
num_examples: 2375
download_size: 6188050025
dataset_size: 6246676265.864
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for Crello
## Table of Contents
- [Dataset Card for Crello](#dataset-card-for-crello)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [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:** [CanvasVAE github](https://github.com/CyberAgentAILab/canvas-vae)
- **Repository:**
- **Paper:** [CanvasVAE: Learning to Generate Vector Graphic Documents](https://arxiv.org/abs/2108.01249)
- **Leaderboard:**
- **Point of Contact:** [Kota Yamaguchi](https://github.com/kyamagu)
### Dataset Summary
The Crello dataset is compiled for the study of vector graphic documents. The dataset contains document meta-data such as canvas size and pre-rendered elements such as images or text boxes. The original templates were collected from [crello.com](https://crello.com) (now [create.vista.com](https://create.vista.com/)) and converted to a low-resolution format suitable for machine learning analysis.
### Usage
```python
import datasets
dataset = datasets.load_dataset("cyberagent/crello")
```
Old revision is available via `revision` option.
```python
import datasets
dataset = datasets.load_dataset("cyberagent/crello", revision="3.1")
```
### Supported Tasks and Leaderboards
[CanvasVAE](https://arxiv.org/abs/2108.01249) studies unsupervised document generation.
### Languages
Almost all design templates use English.
## Dataset Structure
### Data Instances
Each instance has scalar attributes (canvas) and sequence attributes (elements). Categorical values are stored as integer values. Check `ClassLabel` features of the dataset for the list of categorical labels.
```
{'id': '592d6c2c95a7a863ddcda140',
'length': 8,
'group': 4,
'format': 20,
'canvas_width': 3,
'canvas_height': 1,
'category': 0,
'title': 'Beauty Blog Ad Woman with Unusual Hairstyle',
'type': [1, 3, 3, 3, 3, 4, 4, 4],
'left': [0.0,
-0.0009259259095415473,
0.24444444477558136,
0.5712962746620178,
0.2657407522201538,
0.369228333234787,
0.2739444375038147,
0.44776931405067444],
'top': [0.0,
-0.0009259259095415473,
0.37037035822868347,
0.41296297311782837,
0.41296297311782837,
0.8946287035942078,
0.4549448788166046,
0.40591198205947876],
'width': [1.0,
1.0018517971038818,
0.510185182094574,
0.16296295821666718,
0.16296295821666718,
0.30000001192092896,
0.4990740716457367,
0.11388888955116272],
'height': [1.0,
1.0018517971038818,
0.25833332538604736,
0.004629629664123058,
0.004629629664123058,
0.016611294820904732,
0.12458471953868866,
0.02657807245850563],
'opacity': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'text': ['', '', '', '', '', 'STAY WITH US', 'FOLLOW', 'PRESS'],
'font': [0, 0, 0, 0, 0, 152, 172, 152],
'font_size': [0.0, 0.0, 0.0, 0.0, 0.0, 18.0, 135.0, 30.0],
'text_align': [0, 0, 0, 0, 0, 2, 2, 2],
'angle': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
'capitalize': [0, 0, 0, 0, 0, 0, 0, 0],
'line_height': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'letter_spacing': [0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 12.55813980102539, 3.0],
'suitability': [0],
'keywords': ['beautiful',
'beauty',
'blog',
'blogging',
'caucasian',
'cute',
'elegance',
'elegant',
'fashion',
'fashionable',
'femininity',
'glamour',
'hairstyle',
'luxury',
'model',
'stylish',
'vogue',
'website',
'woman',
'post',
'instagram',
'ig',
'insta',
'fashion',
'purple'],
'industries': [1, 8, 13],
'color': [[153.0, 118.0, 96.0],
[34.0, 23.0, 61.0],
[34.0, 23.0, 61.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0]],
'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>]}
```
To get a label for categorical values, use the `int2str` method:
```python
key = "font"
example = dataset[0]
dataset.features[key].int2str(example[key])
```
### Data Fields
In the following, categorical fields are shown as `categorical` type, but the actual storage is `int64`.
**Canvas attributes**
| Field | Type | Shape | Description |
| ------------- | ----------- | ------- | ----------------------------------------------------------------- |
| id | string | () | Template ID from crello.com |
| group | categorical | () | Broad design groups, such as social media posts or blog headers |
| format | categorical | () | Detailed design formats, such as Instagram post or postcard |
| category | categorical | () | Topic category of the design, such as holiday celebration |
| canvas_width | categorical | () | Canvas pixel width |
| canvas_height | categorical | () | Canvas pixel height |
| length | int64 | () | Length of elements |
| suitability | categorical | (None,) | List of display tags, only `mobile` tag exists |
| keywords | string | (None,) | List of keywords associated to this template |
| industries | categorical | (None,) | List of industry tags like `marketingAds` |
| preview | image | () | Preview image of the template for convenience; only for debugging |
| cluster_index | int64 | () | Cluster index used to split the dataset; only for debugging |
**Element attributes**
| Field | Type | Shape | Description |
| -------------- | ----------- | --------- | -------------------------------------------------------------------- |
| type | categorical | (None,) | Element type, such as vector shape, image, or text |
| left | float32 | (None,) | Element left position normalized to [0, 1] range w.r.t. canvas_width |
| top | float32 | (None,) | Element top position normalized to [0, 1] range w.r.t. canvas_height |
| width | float32 | (None,) | Element width normalized to [0, 1] range w.r.t. canvas_width |
| height | float32 | (None,) | Element height normalized to [0, 1] range w.r.t. canvas_height |
| color | int64 | (None, 3) | Extracted main RGB color of the element |
| opacity | float32 | (None,) | Opacity in [0, 1] range |
| image | image | (None,) | Pre-rendered 256x256 preview of the element encoded in PNG format |
| text | string | (None,) | Text content in UTF-8 encoding for text element |
| font | categorical | (None,) | Font family name for text element |
| font_size | float32 | (None,) | Font size (height) in pixels |
| text_align | categorical | (None,) | Horizontal text alignment, left, center, right for text element |
| angle | float32 | (None,) | Element rotation angle (radian) w.r.t. the center of the element |
| capitalize | categorical | (None,) | Binary flag to capitalize letters |
| line_height | float32 | (None,) | Scaling parameter to line height, default is 1.0 |
| letter_spacing | float32 | (None,) | Adjustment parameter for letter spacing, default is 0.0 |
Note that the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.
```
https://create.vista.com/artboard/?template=<template_id>
```
`left` and `top` can be negative because elements can be bigger than the canvas size.
### Data Splits
The Crello dataset has 3 splits: train, validation, and test. The current split is generated based on appearance-based clustering.
| Split | Count |
| --------- | ----- |
| train | 19095 |
| validaton | 1951 |
| test | 2375 |
### Visualization
Each example can be visualized in the following approach using [`skia-python`](https://kyamagu.github.io/skia-python/). Note the following does not guarantee a similar appearance to the original template. Currently, the quality of text rendering is far from perfect.
```python
import io
from typing import Any, Dict
import numpy as np
import skia
def render(features: datasets.Features, example: Dict[str, Any], max_size: float=512.) -> bytes:
"""Render parsed sequence example onto an image and return as PNG bytes."""
canvas_width = int(features["canvas_width"].int2str(example["canvas_width"]))
canvas_height = int(features["canvas_height"].int2str(example["canvas_height"]))
scale = min(1.0, max_size / canvas_width, max_size / canvas_height)
surface = skia.Surface(int(scale * canvas_width), int(scale * canvas_height))
with surface as canvas:
canvas.scale(scale, scale)
for index in range(example["length"]):
pil_image = example["image"][index]
image = skia.Image.frombytes(
pil_image.convert('RGBA').tobytes(),
pil_image.size,
skia.kRGBA_8888_ColorType)
left = example["left"][index] * canvas_width
top = example["top"][index] * canvas_height
width = example["width"][index] * canvas_width
height = example["height"][index] * canvas_height
rect = skia.Rect.MakeXYWH(left, top, width, height)
paint = skia.Paint(Alphaf=example["opacity"][index], AntiAlias=True)
angle = example["angle"][index]
with skia.AutoCanvasRestore(canvas):
if angle != 0:
degree = 180. * angle / np.pi
canvas.rotate(degree, left + width / 2., top + height / 2.)
canvas.drawImageRect(image, rect, paint=paint)
image = surface.makeImageSnapshot()
with io.BytesIO() as f:
image.save(f, skia.kPNG)
return f.getvalue()
```
## Dataset Creation
### Curation Rationale
The Crello dataset is compiled for the general study of vector graphic documents, with the goal of producing a dataset that offers complete vector graphic information suitable for neural methodologies.
### Source Data
#### Initial Data Collection and Normalization
The dataset is initially scraped from the former `crello.com` and pre-processed to the above format.
#### Who are the source language producers?
While [create.vista.com](https://create.vista.com/) owns those templates, the templates seem to be originally created by a specific group of design studios.
### Personal and Sensitive Information
The dataset does not contain any personal information about the creator but may contain a picture of people in the design template.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed for advancing the general study of vector graphic documents, especially for generative systems of graphic design. Successful utilization might enable the automation of creative workflow that human designers get involved in.
### Discussion of Biases
The templates contained in the dataset reflect the biases appearing in the source data, which could present gender biases in specific design categories.
### Other Known Limitations
Due to the unknown data specification of the source data, the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.
https://create.vista.com/artboard/?template=<template_id>
## Additional Information
### Dataset Curators
The Crello dataset was developed by [Kota Yamaguchi](https://github.com/kyamagu).
### Licensing Information
The origin of the dataset is [create.vista.com](https://create.vista.com) (formally, `crello.com`).
The distributor ("We") do not own the copyrights of the original design templates.
By using the Crello dataset, the user of this dataset ("You") must agree to the
[VistaCreate License Agreements](https://create.vista.com/faq/legal/licensing/license_agreements/).
The dataset is distributed under [CDLA-Permissive-2.0 license](https://cdla.dev/permissive-2-0/).
**Note**
We do not re-distribute the original files as we are not allowed by terms.
### Citation Information
@article{yamaguchi2021canvasvae,
title={CanvasVAE: Learning to Generate Vector Graphic Documents},
author={Yamaguchi, Kota},
journal={ICCV},
year={2021}
}
### Releases
4.0.0: v4 release (Dec 5, 2023)
- Change the dataset split based on the template appearance to avoid near-duplicates: no compatibility with v3.
- Class labels have been reordered: no compabilitity with v3.
- Small improvement to font rendering.
3.1: bugfix release (Feb 16, 2023)
- Fix a bug that ignores newline characters in some of the texts.
3.0: v3 release (Feb 13, 2023)
- Migrate to Hugging Face Hub.
- Fix various text rendering bugs.
- Change split generation criteria for avoiding near-duplicates: no compatibility with v2 splits.
- Incorporate a motion picture thumbnail in templates.
- Add `title`, `keywords`, `suitability`, and `industries` canvas attributes.
- Add `capitalize`, `line_height`, and `letter_spacing` element attributes.
2.0: v2 release (May 26, 2022)
- Add `text`, `font`, `font_size`, `text_align`, and `angle` element attributes.
- Include rendered text element in `image_bytes`.
1.0: v1 release (Aug 24, 2021)
### Contributions
Thanks to [@kyamagu](https://github.com/kyamagu) for adding this dataset. | [
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|
laion/wuerstchen-dataset | laion | 2023-12-05T12:11:25Z | 744 | 2 | [
"license:cc0-1.0",
"region:us"
]
| null | 2023-10-12T19:26:49Z | ---
license: cc0-1.0
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
zxvix/Anatomy_Gray | zxvix | 2023-12-06T12:27:33Z | 713 | 0 | [
"region:us"
]
| null | 2023-11-24T12:53:10Z | ---
configs:
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---
# Dataset Card for "Anatomy_Gray"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
sordonia/mmlu-qa-aug-10k-flat | sordonia | 2023-12-05T04:49:49Z | 649 | 0 | [
"region:us"
]
| null | 2023-12-05T03:16:56Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: docno
dtype: string
- name: icl_examples
dtype: 'null'
- name: author_instr
dtype: string
- name: split
dtype: string
- name: source
dtype: string
- name: target
dtype: string
- name: task_name
dtype: string
- name: task_source
dtype: string
splits:
- name: train
num_bytes: 932845961.0306135
num_examples: 570000
download_size: 440382319
dataset_size: 932845961.0306135
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mmlu-qa-aug-10k-flat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
philikai/spider_SQL_PALM_Prompt | philikai | 2023-12-08T12:05:37Z | 641 | 0 | [
"license:cc-by-sa-4.0",
"region:us"
]
| null | 2023-09-07T18:43:07Z | ---
license: cc-by-sa-4.0
---
Dataset for creating prompts for fine-tuning on Spider Dataset with Foreign and Primary Key Information as well as Schema information. | [
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|
open-llm-leaderboard/details_uukuguy__speechless-code-mistral-7b-v1.0 | open-llm-leaderboard | 2023-12-08T00:34:03Z | 464 | 0 | [
"region:us"
]
| null | 2023-10-10T09:36:03Z | ---
pretty_name: Evaluation run of uukuguy/speechless-code-mistral-7b-v1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-code-mistral-7b-v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0)\
\ 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 3 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_uukuguy__speechless-code-mistral-7b-v1.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-08T00:30:08.150409](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-code-mistral-7b-v1.0/blob/main/results_2023-12-08T00-30-08.150409.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\": 0.6328087510139756,\n\
\ \"acc_stderr\": 0.03241662679481022,\n \"acc_norm\": 0.6370452055948831,\n\
\ \"acc_norm_stderr\": 0.033063806342390464,\n \"mc1\": 0.33414932680538556,\n\
\ \"mc1_stderr\": 0.016512530677150538,\n \"mc2\": 0.4789652694280945,\n\
\ \"mc2_stderr\": 0.01485651327015513\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5708191126279863,\n \"acc_stderr\": 0.014464085894870655,\n\
\ \"acc_norm\": 0.6117747440273038,\n \"acc_norm_stderr\": 0.014241614207414044\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6405098585939056,\n\
\ \"acc_stderr\": 0.004788703173474743,\n \"acc_norm\": 0.8376817367058355,\n\
\ \"acc_norm_stderr\": 0.003679889125399815\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\
\ \"acc_stderr\": 0.042849586397534015,\n \"acc_norm\": 0.562962962962963,\n\
\ \"acc_norm_stderr\": 0.042849586397534015\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.03782728980865469,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.03782728980865469\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880274,\n\
\ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880274\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\
\ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n\
\ \"acc_norm_stderr\": 0.03703851193099521\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\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\
acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7580645161290323,\n\
\ \"acc_stderr\": 0.02436259969303108,\n \"acc_norm\": 0.7580645161290323,\n\
\ \"acc_norm_stderr\": 0.02436259969303108\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.028606204289229872,\n \"\
acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229872\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6538461538461539,\n \"acc_stderr\": 0.024121125416941183,\n\
\ \"acc_norm\": 0.6538461538461539,\n \"acc_norm_stderr\": 0.024121125416941183\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \
\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634335,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634335\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\
acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8293577981651377,\n \"acc_stderr\": 0.016129271025099857,\n \"\
acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.016129271025099857\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7745098039215687,\n \"acc_stderr\": 0.02933116229425174,\n \"\
acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.02933116229425174\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808514,\n \
\ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808514\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477518,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477518\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.04330043749650741,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.04330043749650741\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n\
\ \"acc_stderr\": 0.013816335389973147,\n \"acc_norm\": 0.8173690932311622,\n\
\ \"acc_norm_stderr\": 0.013816335389973147\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.34301675977653634,\n\
\ \"acc_stderr\": 0.015876912673057728,\n \"acc_norm\": 0.34301675977653634,\n\
\ \"acc_norm_stderr\": 0.015876912673057728\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n\
\ \"acc_stderr\": 0.02540383297817961,\n \"acc_norm\": 0.7234726688102894,\n\
\ \"acc_norm_stderr\": 0.02540383297817961\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\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\": 0.4954367666232073,\n\
\ \"acc_stderr\": 0.012769704263117528,\n \"acc_norm\": 0.4954367666232073,\n\
\ \"acc_norm_stderr\": 0.012769704263117528\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6748366013071896,\n \"acc_stderr\": 0.01895088677080631,\n \
\ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.01895088677080631\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\": 0.7224489795918367,\n \"acc_stderr\": 0.02866685779027465,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.02866685779027465\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\
\ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\
\ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.33414932680538556,\n\
\ \"mc1_stderr\": 0.016512530677150538,\n \"mc2\": 0.4789652694280945,\n\
\ \"mc2_stderr\": 0.01485651327015513\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7837411207576953,\n \"acc_stderr\": 0.011570614861409348\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4700530705079606,\n \
\ \"acc_stderr\": 0.013747759685444703\n }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|arc:challenge|25_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|arc:challenge|25_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T16_31_08.459023
path:
- '**/details_harness|drop|3_2023-10-28T16-31-08.459023.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T16-31-08.459023.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T16_31_08.459023
path:
- '**/details_harness|gsm8k|5_2023-10-28T16-31-08.459023.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|gsm8k|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hellaswag|10_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hellaswag|10_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-35-40.611521.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-35-40.611521.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T00-30-08.150409.parquet'
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- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T00-30-08.150409.parquet'
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T00-30-08.150409.parquet'
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T00-30-08.150409.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T00-30-08.150409.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T09-35-40.611521.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-08T00-30-08.150409.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T16_31_08.459023
path:
- '**/details_harness|winogrande|5_2023-10-28T16-31-08.459023.parquet'
- split: 2023_12_08T00_30_08.150409
path:
- '**/details_harness|winogrande|5_2023-12-08T00-30-08.150409.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-08T00-30-08.150409.parquet'
- config_name: results
data_files:
- split: 2023_10_10T09_35_40.611521
path:
- results_2023-10-10T09-35-40.611521.parquet
- split: 2023_10_28T16_31_08.459023
path:
- results_2023-10-28T16-31-08.459023.parquet
- split: 2023_12_08T00_30_08.150409
path:
- results_2023-12-08T00-30-08.150409.parquet
- split: latest
path:
- results_2023-12-08T00-30-08.150409.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-code-mistral-7b-v1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [uukuguy/speechless-code-mistral-7b-v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0) 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 3 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_uukuguy__speechless-code-mistral-7b-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-08T00:30:08.150409](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-code-mistral-7b-v1.0/blob/main/results_2023-12-08T00-30-08.150409.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": 0.03241662679481022,
"acc_norm": 0.6370452055948831,
"acc_norm_stderr": 0.033063806342390464,
"mc1": 0.33414932680538556,
"mc1_stderr": 0.016512530677150538,
"mc2": 0.4789652694280945,
"mc2_stderr": 0.01485651327015513
},
"harness|arc:challenge|25": {
"acc": 0.5708191126279863,
"acc_stderr": 0.014464085894870655,
"acc_norm": 0.6117747440273038,
"acc_norm_stderr": 0.014241614207414044
},
"harness|hellaswag|10": {
"acc": 0.6405098585939056,
"acc_stderr": 0.004788703173474743,
"acc_norm": 0.8376817367058355,
"acc_norm_stderr": 0.003679889125399815
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.562962962962963,
"acc_stderr": 0.042849586397534015,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.042849586397534015
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.03782728980865469,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.03782728980865469
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.028727502957880274,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.028727502957880274
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6184971098265896,
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"acc_norm": 0.6184971098265896,
"acc_norm_stderr": 0.03703851193099521
},
"harness|hendrycksTest-college_physics|5": {
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"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
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"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
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"acc_norm": 0.5659574468085107,
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},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4473684210526316,
"acc_stderr": 0.04677473004491199,
"acc_norm": 0.4473684210526316,
"acc_norm_stderr": 0.04677473004491199
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
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"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4074074074074074,
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"acc_norm_stderr": 0.02530590624159063
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm_stderr": 0.044518079590553275
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.69,
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"acc_norm": 0.8963730569948186,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.31851851851851853,
"acc_norm_stderr": 0.02840653309060846
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.029344572500634335,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.029344572500634335
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31125827814569534,
"acc_stderr": 0.03780445850526733,
"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526733
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8293577981651377,
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"acc_norm": 0.8293577981651377,
"acc_norm_stderr": 0.016129271025099857
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5092592592592593,
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"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7745098039215687,
"acc_stderr": 0.02933116229425174,
"acc_norm": 0.7745098039215687,
"acc_norm_stderr": 0.02933116229425174
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7679324894514767,
"acc_stderr": 0.027479744550808514,
"acc_norm": 0.7679324894514767,
"acc_norm_stderr": 0.027479744550808514
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.695067264573991,
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"acc_norm": 0.695067264573991,
"acc_norm_stderr": 0.030898610882477518
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7786259541984732,
"acc_stderr": 0.03641297081313729,
"acc_norm": 0.7786259541984732,
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},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8099173553719008,
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"acc_norm": 0.8099173553719008,
"acc_norm_stderr": 0.03581796951709282
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.04330043749650741,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.04330043749650741
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7361963190184049,
"acc_stderr": 0.034624199316156234,
"acc_norm": 0.7361963190184049,
"acc_norm_stderr": 0.034624199316156234
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5,
"acc_stderr": 0.04745789978762494,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04745789978762494
},
"harness|hendrycksTest-management|5": {
"acc": 0.7572815533980582,
"acc_stderr": 0.04245022486384495,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.04245022486384495
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
"acc_stderr": 0.023086635086841407,
"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.023086635086841407
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.8173690932311622,
"acc_norm_stderr": 0.013816335389973147
},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.34301675977653634,
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"acc_norm": 0.34301675977653634,
"acc_norm_stderr": 0.015876912673057728
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.738562091503268,
"acc_stderr": 0.025160998214292456,
"acc_norm": 0.738562091503268,
"acc_norm_stderr": 0.025160998214292456
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7234726688102894,
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"acc_norm": 0.7234726688102894,
"acc_norm_stderr": 0.02540383297817961
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.75,
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"acc_norm": 0.75,
"acc_norm_stderr": 0.02409347123262133
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4954367666232073,
"acc_stderr": 0.012769704263117528,
"acc_norm": 0.4954367666232073,
"acc_norm_stderr": 0.012769704263117528
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6838235294117647,
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"acc_norm": 0.6838235294117647,
"acc_norm_stderr": 0.028245687391462927
},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm": 0.6748366013071896,
"acc_norm_stderr": 0.01895088677080631
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
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"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302506
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7224489795918367,
"acc_stderr": 0.02866685779027465,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.02866685779027465
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8208955223880597,
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"acc_norm": 0.8208955223880597,
"acc_norm_stderr": 0.027113286753111837
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.034873508801977704,
"acc_norm": 0.86,
"acc_norm_stderr": 0.034873508801977704
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.029547741687640038,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640038
},
"harness|truthfulqa:mc|0": {
"mc1": 0.33414932680538556,
"mc1_stderr": 0.016512530677150538,
"mc2": 0.4789652694280945,
"mc2_stderr": 0.01485651327015513
},
"harness|winogrande|5": {
"acc": 0.7837411207576953,
"acc_stderr": 0.011570614861409348
},
"harness|gsm8k|5": {
"acc": 0.4700530705079606,
"acc_stderr": 0.013747759685444703
}
}
```
### 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
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### Other Known Limitations
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## Additional Information
### Dataset Curators
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### Licensing Information
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### Citation Information
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### Contributions
[More Information Needed] | [
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|
bigbio/bc5cdr | bigbio | 2023-12-07T03:52:56Z | 422 | 2 | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
]
| null | 2022-11-13T22:06:13Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: PUBLIC_DOMAIN_MARK_1p0
pretty_name: BC5CDR
homepage: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
- RELATION_EXTRACTION
---
# Dataset Card for BC5CDR
## Dataset Description
- **Homepage:** http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED,RE
The BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated text corpus of human annotations of all chemicals, diseases and their interactions in 1,500 PubMed articles.
## Citation Information
```
@article{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Jiao Li and
Yueping Sun and
Robin J. Johnson and
Daniela Sciaky and
Chih{-}Hsuan Wei and
Robert Leaman and
Allan Peter Davis and
Carolyn J. Mattingly and
Thomas C. Wiegers and
Zhiyong Lu},
title = {BioCreative {V} {CDR} task corpus: a resource for chemical disease
relation extraction},
journal = {Database J. Biol. Databases Curation},
volume = {2016},
year = {2016},
url = {https://doi.org/10.1093/database/baw068},
doi = {10.1093/database/baw068},
timestamp = {Thu, 13 Aug 2020 12:41:41 +0200},
biburl = {https://dblp.org/rec/journals/biodb/LiSJSWLDMWL16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
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|
Skywork/SkyPile-150B | Skywork | 2023-12-07T06:11:28Z | 403 | 168 | [
"task_categories:text-generation",
"size_categories:100B<n<1T",
"language:zh",
"llm ",
"casual-lm",
"language-modeling",
"arxiv:2310.19341",
"region:us"
]
| [
"text-generation"
]
| 2023-10-23T12:55:10Z | ---
task_categories:
- text-generation
language:
- zh
tags:
- 'llm '
- casual-lm
- language-modeling
pretty_name: SkyPile-150B
size_categories:
- 100B<n<1T
---
# SkyPile-150B
## Dataset Summary
SkyPile-150B is a comprehensive, large-scale Chinese dataset specifically designed for the pre-training of large language models. It is derived from a broad array of publicly accessible Chinese Internet web pages. Rigorous filtering, extensive deduplication, and thorough sensitive data filtering have been employed to ensure its quality. Furthermore, we have utilized advanced tools such as fastText and BERT to filter out low-quality data.
The publicly accessible portion of the SkyPile-150B dataset encompasses approximately 233 million unique web pages, each containing an average of over 1,000 Chinese characters. In total, the dataset includes approximately 150 billion tokens and 620 gigabytes of plain text data.
## Language
The SkyPile-150B dataset is exclusively composed of Chinese data.
## Data Field Explanation
- text: the processed and cleaned text extracted from each page.
## Dataset Safety
We utilized more than 200w rules and the BERT-base model to determine the sensitive data present in the dataset, and subsequently removed any harmful entries we detect.
## Sensitive Information and Bias
Despite our best efforts, SkyPile-150B, given its construction from publicly available web pages, might contain sensitive information such as email addresses, phone numbers, or IP addresses. We have endeavored to minimize this through deduplication and low-quality filtering, but users of SkyPile-150B should remain vigilant.
The Internet is rife with potentially toxic or biased data. We have attempted to mitigate this with specific URL filtering methods, but we encourage users to remain conscious of this potential issue.
## Social Impact of the Dataset
The open-source release of the SkyPile-150B dataset represents our commitment to enhancing access to high-quality web data, which has traditionally been a closely guarded resource among model developers. We believe that this release will foster greater accessibility and the proliferation of high-performance large language models, thereby contributing significantly to the advancement of the field.
## License Agreement
The community usage of SkyPile dataset requires Skywork Community License. The SkyPile dataset supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within Skywork Community License as well as Apache2.0.
## Contact Us and Citation
If you find our work helpful, please feel free to cite our paper~
```
@misc{wei2023skywork,
title={Skywork: A More Open Bilingual Foundation Model},
author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou},
year={2023},
eprint={2310.19341},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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|
EleutherAI/pythia-memorized-evals | EleutherAI | 2023-12-09T08:30:28Z | 397 | 2 | [
"region:us"
]
| null | 2023-03-14T15:11:02Z | ---
dataset_info:
features:
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dtype: int64
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sequence: int64
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dataset_size: 16163592144
configs:
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data_files:
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path: data/duped.12b.23000-*
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path: data/deduped.12b.103000-*
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path: data/deduped.12b.123000-*
---
# Dataset Card for "pythia-memorized-evals"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
ibragim-bad/hs_dev_test | ibragim-bad | 2023-12-07T07:31:00Z | 360 | 0 | [
"region:us"
]
| null | 2023-12-07T07:30:50Z | ---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold_generation
dtype: string
splits:
- name: test
num_bytes: 7885767
num_examples: 10003
- name: dev
num_bytes: 9610103
num_examples: 10042
download_size: 10451785
dataset_size: 17495870
---
# Dataset Card for "hs_dev_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
sinonimayzer/mixed-data | sinonimayzer | 2023-12-05T21:10:04Z | 347 | 0 | [
"region:us"
]
| null | 2023-12-02T05:30:02Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 17829001968.66333
num_examples: 10391742
- name: validation
num_bytes: 990500109.370185
num_examples: 577319
- name: test
num_bytes: 990501825.0596207
num_examples: 577320
download_size: 13102350107
dataset_size: 19810003903.093136
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
UBC-NLP/sparrow | UBC-NLP | 2023-12-11T00:35:49Z | 340 | 0 | [
"task_categories:text-classification",
"language:ace",
"language:amh",
"language:ara",
"language:arq",
"language:ary",
"language:bam",
"language:ban",
"language:bbc",
"language:ben",
"language:bjn",
"language:bos",
"language:bug",
"language:bul",
"language:ces",
"language:dan",
"language:deu",
"language:ell",
"language:eng",
"language:fas",
"language:fil",
"language:fin",
"language:fre",
"language:hau",
"language:heb",
"language:hin",
"language:hrv",
"language:hun",
"language:ibo",
"language:ind",
"language:ita",
"language:jav",
"language:jpn",
"language:kan",
"language:kin",
"language:kor",
"language:mad",
"language:mal",
"language:mar",
"language:min",
"language:mlt",
"language:nij",
"language:nor",
"language:pcm",
"language:pol",
"language:por",
"language:ron",
"language:rus",
"language:slk",
"language:slv",
"language:spa",
"language:sqi",
"language:srp",
"language:sun",
"language:swe",
"language:swh",
"language:tam",
"language:tel",
"language:tha",
"language:tso",
"language:tur",
"language:twi",
"language:vie",
"language:yor",
"language:zho",
"Anti-Social",
"Emotion Recognition",
"Humor Detection",
"Irony",
"Sarcasm",
"Sentiment Analysis",
"Subjectivity Analysis",
"hate speech detection",
"offensive language detection",
"arxiv:2310.14557",
"region:us"
]
| [
"text-classification"
]
| 2023-11-01T19:38:06Z | ---
viewer: false
language:
- ace
- amh
- ara
- arq
- ary
- bam
- ban
- bbc
- ben
- bjn
- bos
- bug
- bul
- ces
- dan
- deu
- ell
- eng
- fas
- fil
- fin
- fre
- hau
- heb
- hin
- hrv
- hun
- ibo
- ind
- ita
- jav
- jpn
- kan
- kin
- kor
- mad
- mal
- mar
- min
- mlt
- nij
- nor
- pcm
- pol
- por
- ron
- rus
- slk
- slv
- spa
- sqi
- srp
- sun
- swe
- swh
- tam
- tel
- tha
- tso
- tur
- twi
- vie
- yor
- zho
tags:
- Anti-Social
- Emotion Recognition
- Humor Detection
- Irony
- Sarcasm
- Sentiment Analysis
- Subjectivity Analysis
- hate speech detection
- offensive language detection
task_categories:
- text-classification
extra_gated_fields:
Name: text
Offical Email: text
Affilation: text
Country: text
I agree to use this dataset for non-commercial use ONLY: checkbox
I agree to cite the SPARROW paper and all original papers: checkbox
---
<p align="center">
<br>
<img src="https://sparrow.dlnlp.ai/img/sparrow_main2.jpg" width="70%"/>
<br>
<p>
<p align="center">
<!-- <a href="https://github.com/UBC-NLP/sparraw/releases"> -->
<!-- <img alt="GitHub release" src="https://img.shields.io/github/release/UBC-NLP/sparraw.svg"> </a>-->
<a href="https://sparrow.dlnlp.ai/">
<img alt="Documentation" src="https://img.shields.io/website.svg?down_color=red&down_message=offline&up_message=online&url=https://sparrow.dlnlp.ai">
</a>
</p>
In this work, we introduce [**SPARROW**](https://arxiv.org/abs/2310.14557), SPARROW is a evaluation benchmark for sociopragmatic meaning understanding. SPARROW comprises 169 datasets covering 13 task types across six primary categories (e.g., anti-social language detection, emotion recognition). SPARROW datasets encompass 64 different languages originating from 12 language families representing 16 writing scripts.
# How to Use SPARROW
### Request Access ###
To obtain access to the SPARROW benchmark on Huggingface, follow the following steps:
- Login on your Haggingface account
<img src="https://sparrow.dlnlp.ai/img/hf_login_request.png" width="70%"/>
- Request access
<img src="https://sparrow.dlnlp.ai/img/sparrow_request.png" width="70%"/>
## Install Requirments
```shell
pip install datasets transformers seqeval
```
### Login with your Huggingface CLI ###
You can get/manage your access tokens in your [settings](https://huggingface.co/docs/hub/security-tokens).
```shell
export HUGGINGFACE_TOKEN=""
huggingface-cli login --token $HUGGINGFACE_TOKEN
```
## Submitting your results on SPARROW test
We design a public leaderboard for scoring PLMs on SPARRAW. Our leaderboard is interactive and offers rich meta-data about the various datasets involved as well as the language models we evaluate.
You can evalute your models using **SPARROW** leaderboard: **[https://sparrow.dlnlp.ai](https://sparrow.dlnlp.ai)**
---
## Citation
If you use SPARROW for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows:
```bibtex
@inproceedings{zhang-etal-2023-skipped,
title = "The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages",
author = "Zhang, Chiyu and
Khai Duy Doan and,
Qisheng Liao and,
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2023",
publisher = "Association for Computational Linguistics",
}
```
---
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). | [
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|
HuggingFaceM4/datikz_modif | HuggingFaceM4 | 2023-12-06T22:44:21Z | 340 | 0 | [
"region:us"
]
| null | 2023-12-06T22:43:51Z | Invalid username or password. | [
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|
paoloitaliani/privacyqa | paoloitaliani | 2023-12-05T09:25:46Z | 324 | 0 | [
"region:us"
]
| null | 2023-10-17T08:55:48Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: full
path: data/full-*
dataset_info:
features:
- name: document
dtype: string
- name: qa_pair
dtype: string
splits:
- name: train
num_bytes: 335038.44083526684
num_examples: 344
- name: validation
num_bytes: 42853.75406032483
num_examples: 44
- name: test
num_bytes: 41879.805104408355
num_examples: 43
- name: full
num_bytes: 419772
num_examples: 431
download_size: 233651
dataset_size: 839544.0
---
# Dataset Card for "privacyqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
oftverse/control-celeba-hq | oftverse | 2023-12-08T14:13:39Z | 302 | 2 | [
"license:mit",
"region:us"
]
| null | 2023-12-04T15:15:03Z | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1087163166.621
num_examples: 29487
- name: test
num_bytes: 18131154.0
num_examples: 500
download_size: 1089858259
dataset_size: 1105294320.621
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for Control-CelebA-HQ
## Overview
**Dataset Name**: Control-CelebA-HQ
**Description**: An enhanced version of the CelebA-HQ dataset, Control-CelebA-HQ is specifically designed for evaluating the controlling ability of controllable generative models. This dataset is featured in the NeurIPS 2023 work titled "Controlling Text-to-Image Diffusion by Orthogonal Finetuning (OFT)", and is pivotal in evaluating the control ability of the controllable generative models.
**Dataset Type**: Generative Model, Controllable Generation, PEFT
**Official Page**: https://oft.wyliu.com/
## Dataset Structure
**Data Format**: Images with paired facial landmarks
**Size**: Training set - 29.5k images; Testing set - 500 images
**Resolution**: High Quality (CelebA-HQ standard)
**Attributes**: Facial features with color-coded facial landmarks for controllable generation
## Data Collection and Preparation
**Source**: Derived from the CelebA-HQ dataset
**Collection Method**: Original CelebA-HQ images processed with a standard face alignment tracker (available at https://github.com/1adrianb/face-alignment) for facial landmark detection
**Data Split**: 29.5k images for training, 500 images for testing
## Dataset Use and Access
**Recommended Uses**: Training and testing controllable generative models, particularly in the context of facial image generation with landmark-based control
**User Guidelines**: To use the dataset, train models on the training set using facial landmarks as control signals. For testing, generate images with landmarks as control and evaluate control consistency error between input and generated image's landmarks. Please cite the OFT paper when using this dataset and protocol.
**Note:** Example usage and evaluation script will come out soon in Huggingface PEFT and Diffusers example. Stay tuned:D
**Citation**:
```
@InProceedings{Qiu2023OFT,
title={Controlling Text-to-Image Diffusion by Orthogonal Finetuning},
author={Qiu, Zeju and Liu, Weiyang and Feng, Haiwen and Xue, Yuxuan and Feng, Yao and Liu, Zhen and Zhang, Dan and Weller, Adrian and Schölkopf, Bernhard},
booktitle={NeurIPS},
year={2023}
}
```
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|
victorcosta/pt_legislation | victorcosta | 2023-12-05T19:38:52Z | 294 | 0 | [
"language:pt",
"region:us"
]
| null | 2023-10-23T19:06:23Z | ---
language: pt
configs:
- config_name: ria_ground_truth
data_files: ria_ground_truth/train.parquet
version: 1.0.5
- config_name: unlabeled
data_files: unlabeled/train.parquet
version: 1.0.0
---
# Portuguese Legislation dataset
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|
ibragim-bad/bigbench-superglue-tsi | ibragim-bad | 2023-12-06T08:11:54Z | 288 | 1 | [
"region:us"
]
| null | 2023-12-06T08:10:34Z | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task
dtype: string
splits:
- name: bigbench
num_bytes: 2889038
num_examples: 5670
- name: superglue
num_bytes: 1046139
num_examples: 2966
- name: tsi
num_bytes: 1981886
num_examples: 5000
download_size: 2835363
dataset_size: 5917063
---
# Dataset Card for "bigbench-superglue-tsi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
sogeeking/vqvae_token_big | sogeeking | 2023-12-05T20:59:21Z | 278 | 0 | [
"region:us"
]
| null | 2023-12-05T16:48:57Z | ---
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|
epfl-llm/guidelines | epfl-llm | 2023-12-05T12:16:57Z | 272 | 45 | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"medical",
"health",
"arxiv:2311.16079",
"region:us"
]
| [
"text-generation"
]
| 2023-11-21T10:35:34Z | ---
license: other
license_name: common-crawl
license_link: LICENSE
task_categories:
- text-generation
language:
- en
pretty_name: Clinical Guidelines
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: guidelines.jsonl
tags:
- medical
- health
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: title
dtype: string
- name: clean_text
dtype: string
- name: raw_text
dtype: string
- name: url
dtype: string
- name: overview
dtype: string
---
# Clinical Guidelines
The Clinical Guidelines corpus is a new dataset of 46K clinical practice guidelines from 16 high-quality online medical sources. This dataset serves as a crucial component of the original training corpus of the [Meditron](https://huggingface.co/epfl-llm/meditron-70b) Large Language Model (LLM). We publicly release a subset of 36K articles from our Guidelines corpus, extracted from 8 of 16 sources that allow content redistribution, namely CCO, CDC, CMA, ICRC, NICE, SPOR, WHO and WikiDoc.
You can scrape and clean all 16 guideline sources using our code in [epfLLM/meditron](https://github.com/epfLLM/meditron).
<img width=75% src="sources.png" alt="Sources of Clinical Practice Guidelines" title="CPG sources">
## Dataset Details
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [EPFL LLM Team](https://huggingface.co/epfl-llm)
- **Language(s):** English only
- **License:** [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use)
- **Repository:** [epfLLM/meditron](https://github.com/epfLLM/meditron)
- **Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079)*
- **Knowledge Cutoff**: August 2023
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The dataset was curated to provide a high-quality collection of clinical practice guidelines (CPGs) for the medical training of LLMs. Our Clinical Guidelines corpus comprises 46,469 articles from 16 globally recognized sources for clinician and patient-directed guidance across high and low-resource settings, multiple medical domains (internal medicine, pediatrics, oncology, infectious disease, etc.) and multiple geographical locations.
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
Clinical practice guidelines are rigorously researched frameworks designed to guide healthcare practitioners and patients in making evidence-based decisions regarding diagnosis, treatment, and management.
They are compiled through a systematic process of collaborative consensus between experts to establish recommendations from the latest evidence on best practices that would maximize benefit in light of practical concerns such as available resources and context. As a super-synthesis of meta-analyses, they sit atop the *evidence pyramid* and form the basis of actionable evidence-based practice.
Clinical guidelines differ based on several factors:
- **Organizational level**: CPGs are produced at various organizational granularities, ranging from global to hospital-level initiatives directed by international professional medical associations to informal consortia, regional or national governmental bodies to individual NGOs and hospitals.
- **Geographic scope**: The geographic scope ranges from global (WHO) to national (CDC, NICE) and regional (Ontario, Melbourne) to institutional (ICRC, Mayo Clinic). This corpus is biased towards English-speaking regions due to its exclusive focus on English content.
- **Resource level**: The corpus also represents health care concerns from high- (Ontario, Melbourne), low- (WHO), and volatile- (ICRC) resource settings.
- **Audience level**: Guidelines also contains a range of technical and conversational vocabulary with target audiences of clinicians or patients (or both), and is sometimes highly specialized within a theme (cancer, pediatrics, infectious disease).
- **Peer-review**: The peer review processes also ranged from UN bodies (WHO), institutional review boards (ICRC), professional associations (AAFP) to publicly crowdsourced knowledge bases (WikiDoc).
- **Document size**: Article length varies widely from very short statements to 100+ page guides.
#### 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. -->
The dataset is sourced from 16 globally recognized medical entities, covering a wide range of healthcare contexts and audiences.
We employed pragmatic selection criteria over medical sources, seeking CPGs that were:
- (1) open-access
- (2) systematically formatted with homogenous textual structure (i.e., in a format in which automated processes could be deployed without excessive risk of misaligning textual sequences)
- (3) in the language predominantly represented by the pre-training corpus of Llama (i.e., English)
- (4) covering a breadth of medical sub-domains, audiences (clinician, nurse, patient), and resource settings (high, low, and humanitarian response settings)
| Source | Full Name | Tag | Guidelines | Words | Audience | Country | Released |
|-|-|-|-|-|-|-|-|
| **[AAFP](https://www.aafp.org)** | American Academy of Family Physicians | `aafp` | 50 | 9.4K | Doctor | USA | No |
| **[CCO](https://www.cancercareontario.ca/en/guidelines-advice)** | Cancer Care Ontario | `cco` | 87 | 199K | Doctor | Canada | **Yes** |
| **[CDC](https://www.cdc.gov/)** | Center for Disease Control and Prevention | `cdc` | 621 | 6.7M | Doctor | USA | **Yes** |
| **[CMA](https://joulecma.ca/)** | Canadian Medical Association | `cma` | 431 | 1.7M | Doctor | Canada | **Yes** |
| **[CPS](https://cps.ca)** | Canadian Paediatric Society | `cps` | 54 | 133K | Doctor | Canada | No |
| **[drugs.com](https://www.drugs.com/)** | Drugs.com | `drugs` | 6548 | 4.1M | Both | International | No |
| **[GuidelineCentral](https://www.guidelinecentral.com/)** | GuidelineCentral | `gc` | 1029 | 1M | Doctor | Mix | No |
| **[ICRC](http://icrc.org/)** | International Committee of the Red Cross | `icrc` | 49 | 1.2M | Doctor | International | **Yes** |
| **[IDSA](https://www.idsociety.org/)** | Infectious Diseases Society of America | `idsa` | 47 | 646K | Doctor | USA | No |
| **[MAGIC](https://magicevidence.org/)** | Making GRADE The Irresistible Choice | `magic` | 52 | 415K | Doctor | Mix | No |
| **[MayoClinic](https://www.mayoclinic.org/)** | MayoClinic | `mayo` | 1100 | 2.2M | Patient | USA | No |
| **[NICE](https://www.nice.org.uk/guidance)** | National Institute for Health and Care Excellence | `nice` | 1656 | 8.1M | Doctor | UK | **Yes** |
| **[RCH](https://www.rch.org.au/clinicalguide/about_rch_cpgs/welcome_to_the_clinical_practice_guidelines/)** | Royal Children's Hospital Melbourne | `rch` | 384 | 410K | Doctor | Australia | No |
| **[SPOR](https://sporevidencealliance.ca/key-activities/cpg-asset-map/cpg-database/)** | Strategy for Patient-Oriented Research | `spor` | 217 | 1.1M | Doctor | Canada | **Yes** |
| **[WHO](https://www.who.int/publications/who-guidelines)** | World Health Organization | `who` | 223 | 3.1M | Both | International | **Yes** |
| **[WikiDoc](https://www.wikidoc.org/)** | WikiDoc | `wikidoc` | 33058 | 34M | Both | International | **Yes** |
#### 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. -->
PDF documents were converted to text using [GROBID](https://github.com/kermitt2/grobid).
After extracting the raw text from each source, we cleaned data with an ad-hoc process to exclude irrelevant or repetitive content that did not contribute to the textual content, such as URLs, references, figures, table delimiters, and ill-formatted characters.
This filtering procedure was performed differently for each source using a sample of 50 articles. Please note that this procedure is not perfect, as it may have removed useful information or kept superfluous content. We provide the `raw_text` for each article if you would like to perform your own cleaning step.
Additionally, the text was standardized to a unified format with hierarchical section headers indicated by `'#'`, homogenous spacing `'\n\n'` separating paragraphs, and normalized lists formatted with `'- '` bullet points.
Finally, all samples were deduplicated using title matching, and articles that were too short or not English were filtered out.
#### 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. -->
As the articles are publicly accessible, no personal or sensitive information is included.
## 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. -->
Each row of the dataset represents one clinical practice guideline article, and consists of the following dataset fields (all strings):
| Field | Description | Sources with field |
|-------------|-------------------------------------------|------------------------------|
| `id` | Unique identifier for each article | All |
| `source` | Source tag (`cco`, `cdc`, `cma`, `icrc`, `nice`, `spor`, `who` or `wikidoc`)| All |
| `title` | Title of the article | CMA, NICE & WikiDoc only |
| `url` | URL of the article | NICE, WikiDoc only |
| `raw_text` | Unprocessed scraped article text | All |
| `clean_text`| Cleaned and formatted article text | All |
| `overview` | Short summary of the article | NICE only |
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
The dataset is intended for use in tasks related to text generation, specifically in the context of clinical practice guidelines. It can be employed for training language models and other natural language processing applications within the healthcare domain.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
- **Redistribution**: Please always check redistribution licenses before using the content as these may also evolve over time. To the best of our knowledge, we are following the redistribution licensing of each source and we invite users to inform us if that is not the case.
- **Malicious use**: We do not support any use of this corpus that may be harmful. Creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. please do not create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- **Peer-Review Quality**: It is important to understand that while most sources are validated by internationally endorsed professional associations, a large proportion of articles are from Wikidoc which contains crowdsourced content. While edits in Wikidoc are generally restricted to expert review, the process of consensus and oversight is different from the traditional rigor of clinical guidelines.
- **Representation**: This corpus is in English, and over-represents English-speaking regions. While we have included WHO and ICRC guidelines for low-resource settings, further work needs to be done to scrape sources from diverse contexts.
- **Temporal scope**: Guidelines are constantly updated and these represent a snapshot of each in August 2023. Please re-scrape for updated content.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
We warmly invite users to help us build a more representative corpus with high-quality peer-reviewed clinical practice guidelines in various languages and representing the full scope of clinical specialties and geographic regions.
We encourage users of this content to be mindful of its current limitations in temporal and geographic scope and we repeat our warning: creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. Please don’t create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice.
## Acknowledgments
The availability of open-access clinical practice guidelines (CPG) was critical to this work, and we thank all the societies listed above. A broader representation of geography, medical specialties, and contexts (especially low-resource settings) could be achieved through more standardized CPG formatting practices to ensure reliable textual extraction (e.g., releasing `.txt` or `.html` versions with structured content). We encourage the CPG community to continue to make these documents available (open-access with permissive licenses for incorporation into large language models) and easily usable.
## Authors
- **Curation**: Mary-Anne Hartley
- **Scraping**: Antoine Bonnet, Alexandre Sallinen, Igor Krawczuk, Kyle Matoba
- **Cleaning**: Antoine Bonnet, Alexandre Sallinen
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the Clinical Guidelines corpus, please cite out work:
```
@misc{chen2023meditron70b,
title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models},
author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
year={2023},
eprint={2311.16079},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@software{epfmedtrn,
author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models},
month = November,
year = 2023,
url = {https://github.com/epfLLM/meditron}
}
``` | [
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|
SJ1999/datatransformer1 | SJ1999 | 2023-12-06T02:22:00Z | 262 | 0 | [
"region:us"
]
| null | 2023-11-22T01:16:09Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 547394
num_examples: 219
download_size: 62186
dataset_size: 547394
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
HuggingFaceH4/cai-conversation-harmless | HuggingFaceH4 | 2023-12-10T02:42:03Z | 247 | 2 | [
"region:us"
]
| null | 2023-12-06T18:14:06Z | ---
configs:
- config_name: default
data_files:
- split: test_sft
path: data/test_sft-*
- split: test_prefs
path: data/test_prefs-*
- split: train_sft
path: data/train_sft-*
- split: train_prefs
path: data/train_prefs-*
dataset_info:
features:
- name: index
dtype: int64
- name: prompt
dtype: string
- name: init_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: init_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: critic_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: critic_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: revision_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: revision_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: test_sft
num_bytes: 3784085
num_examples: 1156
- name: test_prefs
num_bytes: 3816979
num_examples: 1156
- name: train_sft
num_bytes: 68593562
num_examples: 21268
- name: train_prefs
num_bytes: 68442112
num_examples: 21269
download_size: 61229214
dataset_size: 144636738
---
# Dataset Card for "cai-conversation-harmless"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
yuyijiong/LongData-Corpus | yuyijiong | 2023-12-08T07:05:12Z | 217 | 4 | [
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
]
| null | 2023-11-11T13:47:14Z | ---
license: cc-by-nc-4.0
language:
- zh
- en
size_categories:
- 10K<n<100K
---
# Long text dataset for pretraining
* This dataset contains samples with the length greater than 16k, which can be used for pretraining models with extremely long context lengths.
* The dataset is continuously updating.
# 长文本模型预训练数据集
* 此数据集包含长度大于16k的预训练数据，可用于对极长上下文长度的模型进行预训练。
* 数据正在持续增加中
## 中文数据
* 筛选自 [悟道200G开源数据](https://github.com/BAAI-WuDao/Data)、[书生万卷数据集](https://opendatalab.org.cn/OpenDataLab/WanJuan1_dot_0)、
[CCI中文互联网语料库](https://huggingface.co/datasets/BAAI/CCI-Data)
、中文维基百科等，
每条数据长度在16000字以上
## 英文数据
* 筛选自 [SlimPajama-dc]
(https://huggingface.co/datasets/MBZUAI-LLM/SlimPajama-627B-DC)， 每条数据长度在16000个word以上
## Sharegpt长对话
* 包含筛选自sharegpt的长度大于8k字的中文和英文对话。[Sharegpt长对话](https://huggingface.co/datasets/yuyijiong/Sharegpt-long-conversation)
## 图书、小说
中文小说由于数据量太大，已经上传至[云盘](https://cloud.tsinghua.edu.cn/d/0670fcb14d294c97b5cf/)。
英文图书来自RedPajamaBook，筛选了长度大于100k words的图书。
## 注意
注意，有些长文本数据中含有大量的重复字符串，推荐在使用以下代码进行去重：
```python
import re
#删除连续出现4次以上的非数字和字母的字符，只保留4次
content = re.sub(r'([^a-zA-Z0-9])\1{4,}', r'\1\1\1\1', content)
#删除连续出现3次以上的数字和字母组成的子串，例如“121212”变为“12”
content = re.sub(r'([a-zA-Z0-9]{3,}?)\1+', r'\1', content)
```
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|
victorcosta/eu_directives | victorcosta | 2023-12-05T19:32:43Z | 215 | 0 | [
"language:pt",
"region:us"
]
| null | 2023-11-15T20:39:49Z | ---
language: pt
configs:
- config_name: ria_ground_truth
data_files: ria_ground_truth/train.parquet
version: 1.0.5
---
# European Union's Directives dataset (PT)
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|
mozilla-foundation/common_voice_15_0 | mozilla-foundation | 2023-12-07T18:20:14Z | 212 | 2 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
]
| [
"automatic-speech-recognition"
]
| 2023-12-07T10:17:36Z | ---
pretty_name: Common Voice Corpus 15
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language_bcp47:
- ab
- af
- am
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- dyu
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- gl
- gn
- ha
- he
- hi
- hsb
- hu
- hy-AM
- ia
- id
- ig
- is
- it
- ja
- ka
- kab
- kk
- kmr
- ko
- ky
- lg
- lo
- lt
- lv
- mdf
- mhr
- mk
- ml
- mn
- mr
- mrj
- mt
- myv
- nan-tw
- ne-NP
- nl
- nn-NO
- oc
- or
- pa-IN
- pl
- ps
- pt
- quy
- rm-sursilv
- rm-vallader
- ro
- ru
- rw
- sah
- sat
- sc
- sk
- skr
- sl
- sq
- sr
- sv-SE
- sw
- ta
- th
- ti
- tig
- tk
- tok
- tr
- tt
- tw
- ug
- uk
- ur
- uz
- vi
- vot
- yo
- yue
- zgh
- zh-CN
- zh-HK
- zh-TW
license:
- cc0-1.0
multilinguality:
- multilingual
source_datasets:
- extended|common_voice
task_categories:
- automatic-speech-recognition
paperswithcode_id: common-voice
extra_gated_prompt: "By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."
---
# Dataset Card for Common Voice Corpus 15
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Vaibhav Srivastav](mailto:[email protected])
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 28750 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 19159 validated hours in 114 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hebrew, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi):
```python
from datasets import load_dataset
cv_15 = load_dataset("mozilla-foundation/common_voice_15_0", "hi", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
cv_15 = load_dataset("mozilla-foundation/common_voice_15_0", "hi", split="train", streaming=True)
print(next(iter(cv_14)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
### Local
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_15 = load_dataset("mozilla-foundation/common_voice_15_0", "hi", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_15), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_15, batch_sampler=batch_sampler)
```
### Streaming
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_15 = load_dataset("mozilla-foundation/common_voice_15_0", "hi", split="train")
dataloader = DataLoader(cv_15, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_15_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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|
open-llm-leaderboard/details_codellama__CodeLlama-34b-Instruct-hf | open-llm-leaderboard | 2023-12-10T07:36:00Z | 203 | 0 | [
"region:us"
]
| null | 2023-08-25T23:11:36Z | ---
pretty_name: Evaluation run of codellama/CodeLlama-34b-Instruct-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_codellama__CodeLlama-34b-Instruct-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-10T07:31:59.292506](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-34b-Instruct-hf/blob/main/results_2023-12-10T07-31-59.292506.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\": 0.5550583840264827,\n\
\ \"acc_stderr\": 0.03405562001199965,\n \"acc_norm\": 0.5588404318554717,\n\
\ \"acc_norm_stderr\": 0.03476259213185152,\n \"mc1\": 0.28518971848225216,\n\
\ \"mc1_stderr\": 0.015805827874454895,\n \"mc2\": 0.44437538633055657,\n\
\ \"mc2_stderr\": 0.014550940721814704\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5093856655290102,\n \"acc_stderr\": 0.014608816322065,\n\
\ \"acc_norm\": 0.5426621160409556,\n \"acc_norm_stderr\": 0.01455810654392406\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5637323242381995,\n\
\ \"acc_stderr\": 0.004949080334816024,\n \"acc_norm\": 0.7691694881497709,\n\
\ \"acc_norm_stderr\": 0.004205030476886528\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\
\ \"acc_stderr\": 0.04304979692464244,\n \"acc_norm\": 0.45925925925925926,\n\
\ \"acc_norm_stderr\": 0.04304979692464244\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5921052631578947,\n \"acc_stderr\": 0.03999309712777472,\n\
\ \"acc_norm\": 0.5921052631578947,\n \"acc_norm_stderr\": 0.03999309712777472\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\
\ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.49056603773584906,\n \"acc_stderr\": 0.0307673947078081,\n\
\ \"acc_norm\": 0.49056603773584906,\n \"acc_norm_stderr\": 0.0307673947078081\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5138888888888888,\n\
\ \"acc_stderr\": 0.04179596617581,\n \"acc_norm\": 0.5138888888888888,\n\
\ \"acc_norm_stderr\": 0.04179596617581\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \
\ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4508670520231214,\n\
\ \"acc_stderr\": 0.03794012674697028,\n \"acc_norm\": 0.4508670520231214,\n\
\ \"acc_norm_stderr\": 0.03794012674697028\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.047240073523838876,\n\
\ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.047240073523838876\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.49361702127659574,\n \"acc_stderr\": 0.03268335899936336,\n\
\ \"acc_norm\": 0.49361702127659574,\n \"acc_norm_stderr\": 0.03268335899936336\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n\
\ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3915343915343915,\n \"acc_stderr\": 0.025138091388851102,\n \"\
acc_norm\": 0.3915343915343915,\n \"acc_norm_stderr\": 0.025138091388851102\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6096774193548387,\n\
\ \"acc_stderr\": 0.027751256636969576,\n \"acc_norm\": 0.6096774193548387,\n\
\ \"acc_norm_stderr\": 0.027751256636969576\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3891625615763547,\n \"acc_stderr\": 0.034304624161038716,\n\
\ \"acc_norm\": 0.3891625615763547,\n \"acc_norm_stderr\": 0.034304624161038716\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\": 0.6727272727272727,\n \"acc_stderr\": 0.036639749943912434,\n\
\ \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.036639749943912434\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7222222222222222,\n \"acc_stderr\": 0.03191178226713549,\n \"\
acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.03191178226713549\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7616580310880829,\n \"acc_stderr\": 0.03074890536390989,\n\
\ \"acc_norm\": 0.7616580310880829,\n \"acc_norm_stderr\": 0.03074890536390989\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5153846153846153,\n \"acc_stderr\": 0.02533900301010651,\n \
\ \"acc_norm\": 0.5153846153846153,\n \"acc_norm_stderr\": 0.02533900301010651\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113114,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113114\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5168067226890757,\n \"acc_stderr\": 0.03246013680375308,\n \
\ \"acc_norm\": 0.5168067226890757,\n \"acc_norm_stderr\": 0.03246013680375308\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7064220183486238,\n \"acc_stderr\": 0.019525151122639667,\n \"\
acc_norm\": 0.7064220183486238,\n \"acc_norm_stderr\": 0.019525151122639667\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.033247089118091176,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.033247089118091176\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7794117647058824,\n \"acc_stderr\": 0.029102254389674082,\n \"\
acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.029102254389674082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159256,\n \
\ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159256\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6098654708520179,\n\
\ \"acc_stderr\": 0.03273766725459156,\n \"acc_norm\": 0.6098654708520179,\n\
\ \"acc_norm_stderr\": 0.03273766725459156\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6259541984732825,\n \"acc_stderr\": 0.042438692422305246,\n\
\ \"acc_norm\": 0.6259541984732825,\n \"acc_norm_stderr\": 0.042438692422305246\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7355371900826446,\n \"acc_stderr\": 0.040261875275912046,\n \"\
acc_norm\": 0.7355371900826446,\n \"acc_norm_stderr\": 0.040261875275912046\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6993865030674846,\n \"acc_stderr\": 0.03602511318806771,\n\
\ \"acc_norm\": 0.6993865030674846,\n \"acc_norm_stderr\": 0.03602511318806771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\
\ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\
\ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.046561471100123514,\n\
\ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.046561471100123514\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\": 0.56,\n \"acc_stderr\": 0.0498887651569859,\n \
\ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7075351213282248,\n\
\ \"acc_stderr\": 0.016267000684598645,\n \"acc_norm\": 0.7075351213282248,\n\
\ \"acc_norm_stderr\": 0.016267000684598645\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5780346820809249,\n \"acc_stderr\": 0.02658923114217426,\n\
\ \"acc_norm\": 0.5780346820809249,\n \"acc_norm_stderr\": 0.02658923114217426\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31620111731843575,\n\
\ \"acc_stderr\": 0.015551673652172554,\n \"acc_norm\": 0.31620111731843575,\n\
\ \"acc_norm_stderr\": 0.015551673652172554\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5915032679738562,\n \"acc_stderr\": 0.028146405993096358,\n\
\ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.028146405993096358\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.594855305466238,\n\
\ \"acc_stderr\": 0.027882383791325963,\n \"acc_norm\": 0.594855305466238,\n\
\ \"acc_norm_stderr\": 0.027882383791325963\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6141975308641975,\n \"acc_stderr\": 0.027085401226132143,\n\
\ \"acc_norm\": 0.6141975308641975,\n \"acc_norm_stderr\": 0.027085401226132143\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.40425531914893614,\n \"acc_stderr\": 0.029275532159704725,\n \
\ \"acc_norm\": 0.40425531914893614,\n \"acc_norm_stderr\": 0.029275532159704725\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3891786179921773,\n\
\ \"acc_stderr\": 0.012452613934287012,\n \"acc_norm\": 0.3891786179921773,\n\
\ \"acc_norm_stderr\": 0.012452613934287012\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.47794117647058826,\n \"acc_stderr\": 0.030343264224213535,\n\
\ \"acc_norm\": 0.47794117647058826,\n \"acc_norm_stderr\": 0.030343264224213535\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.020227834851568375,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.020227834851568375\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.044262946482000985,\n\
\ \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.044262946482000985\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6489795918367347,\n\
\ \"acc_stderr\": 0.03055531675557364,\n \"acc_norm\": 0.6489795918367347,\n\
\ \"acc_norm_stderr\": 0.03055531675557364\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.7611940298507462,\n \"acc_stderr\": 0.03014777593540922,\n\
\ \"acc_norm\": 0.7611940298507462,\n \"acc_norm_stderr\": 0.03014777593540922\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.041633319989322626\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\": 0.7485380116959064,\n\
\ \"acc_stderr\": 0.033275044238468436,\n \"acc_norm\": 0.7485380116959064,\n\
\ \"acc_norm_stderr\": 0.033275044238468436\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 0.28518971848225216,\n \"mc1_stderr\": 0.015805827874454895,\n\
\ \"mc2\": 0.44437538633055657,\n \"mc2_stderr\": 0.014550940721814704\n\
\ },\n \"harness|winogrande|5\": {\n \"acc\": 0.7458563535911602,\n\
\ \"acc_stderr\": 0.012236307219708267\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.379833206974981,\n \"acc_stderr\": 0.013368818096960495\n\
\ }\n}\n```"
repo_url: https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|arc:challenge|25_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|arc:challenge|25_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_05T07_42_59.568400
path:
- '**/details_harness|drop|3_2023-11-05T07-42-59.568400.parquet'
- split: 2023_11_07T04_11_54.628433
path:
- '**/details_harness|drop|3_2023-11-07T04-11-54.628433.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-07T04-11-54.628433.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_05T07_42_59.568400
path:
- '**/details_harness|gsm8k|5_2023-11-05T07-42-59.568400.parquet'
- split: 2023_11_07T04_11_54.628433
path:
- '**/details_harness|gsm8k|5_2023-11-07T04-11-54.628433.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|gsm8k|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hellaswag|10_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hellaswag|10_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-25T23:11:14.511248.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-25T23:11:14.511248.parquet'
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- split: 2023_12_10T07_31_59.292506
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-25T23:11:14.511248.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T07-31-59.292506.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_05T07_42_59.568400
path:
- '**/details_harness|winogrande|5_2023-11-05T07-42-59.568400.parquet'
- split: 2023_11_07T04_11_54.628433
path:
- '**/details_harness|winogrande|5_2023-11-07T04-11-54.628433.parquet'
- split: 2023_12_10T07_31_59.292506
path:
- '**/details_harness|winogrande|5_2023-12-10T07-31-59.292506.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-10T07-31-59.292506.parquet'
- config_name: results
data_files:
- split: 2023_08_25T23_11_14.511248
path:
- results_2023-08-25T23:11:14.511248.parquet
- split: 2023_11_05T07_42_59.568400
path:
- results_2023-11-05T07-42-59.568400.parquet
- split: 2023_11_07T04_11_54.628433
path:
- results_2023-11-07T04-11-54.628433.parquet
- split: 2023_12_10T07_31_59.292506
path:
- results_2023-12-10T07-31-59.292506.parquet
- split: latest
path:
- results_2023-12-10T07-31-59.292506.parquet
---
# Dataset Card for Evaluation run of codellama/CodeLlama-34b-Instruct-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_codellama__CodeLlama-34b-Instruct-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-10T07:31:59.292506](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-34b-Instruct-hf/blob/main/results_2023-12-10T07-31-59.292506.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": 0.5550583840264827,
"acc_stderr": 0.03405562001199965,
"acc_norm": 0.5588404318554717,
"acc_norm_stderr": 0.03476259213185152,
"mc1": 0.28518971848225216,
"mc1_stderr": 0.015805827874454895,
"mc2": 0.44437538633055657,
"mc2_stderr": 0.014550940721814704
},
"harness|arc:challenge|25": {
"acc": 0.5093856655290102,
"acc_stderr": 0.014608816322065,
"acc_norm": 0.5426621160409556,
"acc_norm_stderr": 0.01455810654392406
},
"harness|hellaswag|10": {
"acc": 0.5637323242381995,
"acc_stderr": 0.004949080334816024,
"acc_norm": 0.7691694881497709,
"acc_norm_stderr": 0.004205030476886528
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.45925925925925926,
"acc_stderr": 0.04304979692464244,
"acc_norm": 0.45925925925925926,
"acc_norm_stderr": 0.04304979692464244
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5921052631578947,
"acc_stderr": 0.03999309712777472,
"acc_norm": 0.5921052631578947,
"acc_norm_stderr": 0.03999309712777472
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.49056603773584906,
"acc_stderr": 0.0307673947078081,
"acc_norm": 0.49056603773584906,
"acc_norm_stderr": 0.0307673947078081
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.04179596617581,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.04179596617581
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.45,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_stderr": 0.03794012674697028,
"acc_norm": 0.4508670520231214,
"acc_norm_stderr": 0.03794012674697028
},
"harness|hendrycksTest-college_physics|5": {
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"acc_stderr": 0.047240073523838876,
"acc_norm": 0.3431372549019608,
"acc_norm_stderr": 0.047240073523838876
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
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},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_norm": 0.49361702127659574,
"acc_norm_stderr": 0.03268335899936336
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.046446020912223177,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.046446020912223177
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.041618085035015295
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"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.3915343915343915,
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"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.36,
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},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.6096774193548387,
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},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3891625615763547,
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"acc_norm": 0.3891625615763547,
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},
"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.34074074074074073,
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5168067226890757,
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"acc_norm": 0.5168067226890757,
"acc_norm_stderr": 0.03246013680375308
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"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7064220183486238,
"acc_stderr": 0.019525151122639667,
"acc_norm": 0.7064220183486238,
"acc_norm_stderr": 0.019525151122639667
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.033247089118091176,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.033247089118091176
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7794117647058824,
"acc_stderr": 0.029102254389674082,
"acc_norm": 0.7794117647058824,
"acc_norm_stderr": 0.029102254389674082
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7637130801687764,
"acc_stderr": 0.027652153144159256,
"acc_norm": 0.7637130801687764,
"acc_norm_stderr": 0.027652153144159256
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6098654708520179,
"acc_stderr": 0.03273766725459156,
"acc_norm": 0.6098654708520179,
"acc_norm_stderr": 0.03273766725459156
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6259541984732825,
"acc_stderr": 0.042438692422305246,
"acc_norm": 0.6259541984732825,
"acc_norm_stderr": 0.042438692422305246
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7355371900826446,
"acc_stderr": 0.040261875275912046,
"acc_norm": 0.7355371900826446,
"acc_norm_stderr": 0.040261875275912046
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.04557239513497751,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.04557239513497751
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6993865030674846,
"acc_stderr": 0.03602511318806771,
"acc_norm": 0.6993865030674846,
"acc_norm_stderr": 0.03602511318806771
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.38392857142857145,
"acc_stderr": 0.04616143075028547,
"acc_norm": 0.38392857142857145,
"acc_norm_stderr": 0.04616143075028547
},
"harness|hendrycksTest-management|5": {
"acc": 0.6699029126213593,
"acc_stderr": 0.046561471100123514,
"acc_norm": 0.6699029126213593,
"acc_norm_stderr": 0.046561471100123514
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.811965811965812,
"acc_stderr": 0.025598193686652265,
"acc_norm": 0.811965811965812,
"acc_norm_stderr": 0.025598193686652265
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.56,
"acc_stderr": 0.0498887651569859,
"acc_norm": 0.56,
"acc_norm_stderr": 0.0498887651569859
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7075351213282248,
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.31620111731843575,
"acc_stderr": 0.015551673652172554,
"acc_norm": 0.31620111731843575,
"acc_norm_stderr": 0.015551673652172554
},
"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.5915032679738562,
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},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.594855305466238,
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},
"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3891786179921773,
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"acc_norm_stderr": 0.012452613934287012
},
"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-public_relations|5": {
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"acc_norm_stderr": 0.044262946482000985
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.6489795918367347,
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"acc_norm": 0.6489795918367347,
"acc_norm_stderr": 0.03055531675557364
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"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.7611940298507462,
"acc_norm_stderr": 0.03014777593540922
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.78,
"acc_norm_stderr": 0.041633319989322626
},
"harness|hendrycksTest-virology|5": {
"acc": 0.42168674698795183,
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"acc_norm": 0.42168674698795183,
"acc_norm_stderr": 0.03844453181770917
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7485380116959064,
"acc_stderr": 0.033275044238468436,
"acc_norm": 0.7485380116959064,
"acc_norm_stderr": 0.033275044238468436
},
"harness|truthfulqa:mc|0": {
"mc1": 0.28518971848225216,
"mc1_stderr": 0.015805827874454895,
"mc2": 0.44437538633055657,
"mc2_stderr": 0.014550940721814704
},
"harness|winogrande|5": {
"acc": 0.7458563535911602,
"acc_stderr": 0.012236307219708267
},
"harness|gsm8k|5": {
"acc": 0.379833206974981,
"acc_stderr": 0.013368818096960495
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1 | open-llm-leaderboard | 2023-12-09T22:52:40Z | 198 | 0 | [
"region:us"
]
| null | 2023-10-04T07:21:12Z | ---
pretty_name: Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Riiid/sheep-duck-llama-2-70b-v1.1](https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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_Riiid__sheep-duck-llama-2-70b-v1.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-09T22:48:41.234684](https://huggingface.co/datasets/open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1/blob/main/results_2023-12-09T22-48-41.234684.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\": 0.7053343320815155,\n\
\ \"acc_stderr\": 0.030260160101824644,\n \"acc_norm\": 0.7109334613998801,\n\
\ \"acc_norm_stderr\": 0.03084136530304881,\n \"mc1\": 0.4663402692778458,\n\
\ \"mc1_stderr\": 0.017463793867168103,\n \"mc2\": 0.6454578975757769,\n\
\ \"mc2_stderr\": 0.014741040304266572\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6868600682593856,\n \"acc_stderr\": 0.013552671543623501,\n\
\ \"acc_norm\": 0.7312286689419796,\n \"acc_norm_stderr\": 0.012955065963710688\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.691396136227843,\n\
\ \"acc_stderr\": 0.004609731925736905,\n \"acc_norm\": 0.8777136028679546,\n\
\ \"acc_norm_stderr\": 0.0032694673590543157\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8026315789473685,\n \"acc_stderr\": 0.03238981601699397,\n\
\ \"acc_norm\": 0.8026315789473685,\n \"acc_norm_stderr\": 0.03238981601699397\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7433962264150943,\n \"acc_stderr\": 0.026880647889051985,\n\
\ \"acc_norm\": 0.7433962264150943,\n \"acc_norm_stderr\": 0.026880647889051985\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\
\ \"acc_stderr\": 0.03216600808802267,\n \"acc_norm\": 0.8194444444444444,\n\
\ \"acc_norm_stderr\": 0.03216600808802267\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n\
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\
\ \"acc_stderr\": 0.03514942551267439,\n \"acc_norm\": 0.6936416184971098,\n\
\ \"acc_norm_stderr\": 0.03514942551267439\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\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6893617021276596,\n \"acc_stderr\": 0.03025123757921317,\n\
\ \"acc_norm\": 0.6893617021276596,\n \"acc_norm_stderr\": 0.03025123757921317\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.04692008381368909,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.04692008381368909\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.04013124195424386,\n\
\ \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.04013124195424386\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.47354497354497355,\n \"acc_stderr\": 0.02571523981134676,\n \"\
acc_norm\": 0.47354497354497355,\n \"acc_norm_stderr\": 0.02571523981134676\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8193548387096774,\n\
\ \"acc_stderr\": 0.021886178567172523,\n \"acc_norm\": 0.8193548387096774,\n\
\ \"acc_norm_stderr\": 0.021886178567172523\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5566502463054187,\n \"acc_stderr\": 0.03495334582162933,\n\
\ \"acc_norm\": 0.5566502463054187,\n \"acc_norm_stderr\": 0.03495334582162933\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\
: 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066573,\n\
\ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066573\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8888888888888888,\n \"acc_stderr\": 0.02239078763821677,\n \"\
acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02239078763821677\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9430051813471503,\n \"acc_stderr\": 0.01673108529360755,\n\
\ \"acc_norm\": 0.9430051813471503,\n \"acc_norm_stderr\": 0.01673108529360755\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.022939925418530616,\n\
\ \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.022939925418530616\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524586,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524586\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7689075630252101,\n \"acc_stderr\": 0.027381406927868876,\n\
\ \"acc_norm\": 0.7689075630252101,\n \"acc_norm_stderr\": 0.027381406927868876\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4768211920529801,\n \"acc_stderr\": 0.04078093859163083,\n \"\
acc_norm\": 0.4768211920529801,\n \"acc_norm_stderr\": 0.04078093859163083\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8990825688073395,\n \"acc_stderr\": 0.012914673545364408,\n \"\
acc_norm\": 0.8990825688073395,\n \"acc_norm_stderr\": 0.012914673545364408\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6018518518518519,\n \"acc_stderr\": 0.033384734032074016,\n \"\
acc_norm\": 0.6018518518518519,\n \"acc_norm_stderr\": 0.033384734032074016\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9264705882352942,\n \"acc_stderr\": 0.01831885585008968,\n \"\
acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.01831885585008968\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8818565400843882,\n \"acc_stderr\": 0.021011052659878467,\n \
\ \"acc_norm\": 0.8818565400843882,\n \"acc_norm_stderr\": 0.021011052659878467\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n\
\ \"acc_stderr\": 0.026936111912802273,\n \"acc_norm\": 0.7982062780269058,\n\
\ \"acc_norm_stderr\": 0.026936111912802273\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.0321782942074463,\n\
\ \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.0321782942074463\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8677685950413223,\n \"acc_stderr\": 0.03092278832044579,\n \"\
acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.03092278832044579\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\
\ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\
\ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8466257668711656,\n \"acc_stderr\": 0.0283116014414386,\n\
\ \"acc_norm\": 0.8466257668711656,\n \"acc_norm_stderr\": 0.0283116014414386\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5446428571428571,\n\
\ \"acc_stderr\": 0.04726835553719098,\n \"acc_norm\": 0.5446428571428571,\n\
\ \"acc_norm_stderr\": 0.04726835553719098\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9230769230769231,\n\
\ \"acc_stderr\": 0.01745698787243618,\n \"acc_norm\": 0.9230769230769231,\n\
\ \"acc_norm_stderr\": 0.01745698787243618\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542126,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542126\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8684546615581098,\n\
\ \"acc_stderr\": 0.01208670521425043,\n \"acc_norm\": 0.8684546615581098,\n\
\ \"acc_norm_stderr\": 0.01208670521425043\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7745664739884393,\n \"acc_stderr\": 0.022497230190967554,\n\
\ \"acc_norm\": 0.7745664739884393,\n \"acc_norm_stderr\": 0.022497230190967554\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6346368715083799,\n\
\ \"acc_stderr\": 0.0161048338801423,\n \"acc_norm\": 0.6346368715083799,\n\
\ \"acc_norm_stderr\": 0.0161048338801423\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n\
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7717041800643086,\n\
\ \"acc_stderr\": 0.023839303311398205,\n \"acc_norm\": 0.7717041800643086,\n\
\ \"acc_norm_stderr\": 0.023839303311398205\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8271604938271605,\n \"acc_stderr\": 0.021038517770157375,\n\
\ \"acc_norm\": 0.8271604938271605,\n \"acc_norm_stderr\": 0.021038517770157375\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5886524822695035,\n \"acc_stderr\": 0.029354911159940968,\n \
\ \"acc_norm\": 0.5886524822695035,\n \"acc_norm_stderr\": 0.029354911159940968\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5880052151238592,\n\
\ \"acc_stderr\": 0.012570871032146064,\n \"acc_norm\": 0.5880052151238592,\n\
\ \"acc_norm_stderr\": 0.012570871032146064\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.02679956202488766,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.02679956202488766\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7663398692810458,\n \"acc_stderr\": 0.017119158496044506,\n \
\ \"acc_norm\": 0.7663398692810458,\n \"acc_norm_stderr\": 0.017119158496044506\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7545454545454545,\n\
\ \"acc_stderr\": 0.04122066502878285,\n \"acc_norm\": 0.7545454545454545,\n\
\ \"acc_norm_stderr\": 0.04122066502878285\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7959183673469388,\n \"acc_stderr\": 0.025801283475090496,\n\
\ \"acc_norm\": 0.7959183673469388,\n \"acc_norm_stderr\": 0.025801283475090496\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8756218905472637,\n\
\ \"acc_stderr\": 0.023335401790166327,\n \"acc_norm\": 0.8756218905472637,\n\
\ \"acc_norm_stderr\": 0.023335401790166327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4663402692778458,\n\
\ \"mc1_stderr\": 0.017463793867168103,\n \"mc2\": 0.6454578975757769,\n\
\ \"mc2_stderr\": 0.014741040304266572\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8310970797158642,\n \"acc_stderr\": 0.010529981411838897\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4799090219863533,\n \
\ \"acc_stderr\": 0.013761361772989008\n }\n}\n```"
repo_url: https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|arc:challenge|25_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|arc:challenge|25_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|gsm8k|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hellaswag|10_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hellaswag|10_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T22-48-41.234684.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T22-48-41.234684.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T07-20-47.805368.parquet'
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T22-48-41.234684.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_09T22_48_41.234684
path:
- '**/details_harness|winogrande|5_2023-12-09T22-48-41.234684.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-09T22-48-41.234684.parquet'
- config_name: results
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- results_2023-10-04T07-20-47.805368.parquet
- split: 2023_12_09T22_48_41.234684
path:
- results_2023-12-09T22-48-41.234684.parquet
- split: latest
path:
- results_2023-12-09T22-48-41.234684.parquet
---
# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Riiid/sheep-duck-llama-2-70b-v1.1](https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_Riiid__sheep-duck-llama-2-70b-v1.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-09T22:48:41.234684](https://huggingface.co/datasets/open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1/blob/main/results_2023-12-09T22-48-41.234684.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": 0.7053343320815155,
"acc_stderr": 0.030260160101824644,
"acc_norm": 0.7109334613998801,
"acc_norm_stderr": 0.03084136530304881,
"mc1": 0.4663402692778458,
"mc1_stderr": 0.017463793867168103,
"mc2": 0.6454578975757769,
"mc2_stderr": 0.014741040304266572
},
"harness|arc:challenge|25": {
"acc": 0.6868600682593856,
"acc_stderr": 0.013552671543623501,
"acc_norm": 0.7312286689419796,
"acc_norm_stderr": 0.012955065963710688
},
"harness|hellaswag|10": {
"acc": 0.691396136227843,
"acc_stderr": 0.004609731925736905,
"acc_norm": 0.8777136028679546,
"acc_norm_stderr": 0.0032694673590543157
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.04171654161354543,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.04171654161354543
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8026315789473685,
"acc_stderr": 0.03238981601699397,
"acc_norm": 0.8026315789473685,
"acc_norm_stderr": 0.03238981601699397
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7433962264150943,
"acc_stderr": 0.026880647889051985,
"acc_norm": 0.7433962264150943,
"acc_norm_stderr": 0.026880647889051985
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.03216600808802267,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.03216600808802267
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6936416184971098,
"acc_stderr": 0.03514942551267439,
"acc_norm": 0.6936416184971098,
"acc_norm_stderr": 0.03514942551267439
},
"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": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6893617021276596,
"acc_stderr": 0.03025123757921317,
"acc_norm": 0.6893617021276596,
"acc_norm_stderr": 0.03025123757921317
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.04692008381368909,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.04692008381368909
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6344827586206897,
"acc_stderr": 0.04013124195424386,
"acc_norm": 0.6344827586206897,
"acc_norm_stderr": 0.04013124195424386
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.47354497354497355,
"acc_stderr": 0.02571523981134676,
"acc_norm": 0.47354497354497355,
"acc_norm_stderr": 0.02571523981134676
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5079365079365079,
"acc_stderr": 0.044715725362943486,
"acc_norm": 0.5079365079365079,
"acc_norm_stderr": 0.044715725362943486
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8193548387096774,
"acc_stderr": 0.021886178567172523,
"acc_norm": 0.8193548387096774,
"acc_norm_stderr": 0.021886178567172523
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5566502463054187,
"acc_stderr": 0.03495334582162933,
"acc_norm": 0.5566502463054187,
"acc_norm_stderr": 0.03495334582162933
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8545454545454545,
"acc_stderr": 0.027530196355066573,
"acc_norm": 0.8545454545454545,
"acc_norm_stderr": 0.027530196355066573
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.02239078763821677,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.02239078763821677
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9430051813471503,
"acc_stderr": 0.01673108529360755,
"acc_norm": 0.9430051813471503,
"acc_norm_stderr": 0.01673108529360755
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7128205128205128,
"acc_stderr": 0.022939925418530616,
"acc_norm": 0.7128205128205128,
"acc_norm_stderr": 0.022939925418530616
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.028661201116524586,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.028661201116524586
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7689075630252101,
"acc_stderr": 0.027381406927868876,
"acc_norm": 0.7689075630252101,
"acc_norm_stderr": 0.027381406927868876
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4768211920529801,
"acc_stderr": 0.04078093859163083,
"acc_norm": 0.4768211920529801,
"acc_norm_stderr": 0.04078093859163083
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8990825688073395,
"acc_stderr": 0.012914673545364408,
"acc_norm": 0.8990825688073395,
"acc_norm_stderr": 0.012914673545364408
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6018518518518519,
"acc_stderr": 0.033384734032074016,
"acc_norm": 0.6018518518518519,
"acc_norm_stderr": 0.033384734032074016
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}
```
### 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] | [
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|
tomas-gajarsky/cifar10-lt | tomas-gajarsky | 2023-12-10T22:01:29Z | 193 | 1 | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:cifar10",
"language:en",
"license:apache-2.0",
"region:us"
]
| [
"image-classification"
]
| 2023-05-06T11:56:18Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- cifar10
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-10
pretty_name: Cifar10-LT
dataset_info:
features:
- name: img
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': automobile
'2': bird
'3': cat
'4': deer
'5': dog
'6': frog
'7': horse
'8': ship
'9': truck
config_name: cifar10
splits:
- name: train
- name: test
num_bytes: 22772838
num_examples: 10000
download_size: 170498071
---
# Dataset Card for CIFAR-10-LT (Long Tail)
## 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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Paper:** [Paper imbalanced example](https://openaccess.thecvf.com/content_CVPR_2019/papers/Cui_Class-Balanced_Loss_Based_on_Effective_Number_of_Samples_CVPR_2019_paper.pdf)
- **Leaderboard:** [r-10](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-10) [r-100](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-100)
### Dataset Summary
The CIFAR-10-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 10 distinct classes.
The number of samples within each class decreases exponentially with factors of 10 and 100.
The dataset includes 10,000 test images, with 1000 images per class,
and fewer than 50,000 training images.
Each image is assigned one label.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-100).
### Languages
English
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'label': 0
}
```
### Data Fields
- img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- label: 0-9 with the following correspondence
0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck
### Data Splits
| name |train|test|
|----------|----:|---------:|
|cifar10|<50000| 10000|
### Licensing Information
Apache License 2.0
### Citation Information
```
@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchablani) and all contributors for adding the original balanced cifar10 dataset. | [
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|
ibragim-bad/flan_validation_sample | ibragim-bad | 2023-12-06T09:52:28Z | 191 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T08:13:18Z | ---
license: apache-2.0
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task
dtype: string
splits:
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num_bytes: 8650631.405699916
num_examples: 10307
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- name: clean_5k
num_bytes: 2799557
num_examples: 5000
download_size: 1673085
dataset_size: 17057529.865046103
---
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|
wis-k/instruction-following-eval | wis-k | 2023-12-05T08:38:18Z | 176 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T08:35:26Z | ---
license: apache-2.0
---
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|
abdulrahman-nuzha/squad_v2_prompt | abdulrahman-nuzha | 2023-12-07T10:15:42Z | 175 | 0 | [
"region:us"
]
| null | 2023-11-19T17:38:23Z | ---
dataset_info:
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features:
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dtype: string
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download_size: 158989619
dataset_size: 373381288
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features:
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splits:
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dataset_size: 171115565
configs:
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data_files:
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path: data/train-*
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path: data/test-*
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path: data/validation-*
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data_files:
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path: train/train-*
---
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|
ekolasky/DWIEForCustomLEDConsol | ekolasky | 2023-12-06T17:30:37Z | 166 | 0 | [
"region:us"
]
| null | 2023-12-06T17:30:33Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: result_labels
sequence: int64
- name: grouping_vector
sequence:
sequence: int64
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 4305967
num_examples: 500
- name: validation
num_bytes: 876462
num_examples: 96
download_size: 925707
dataset_size: 5182429
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
chenglu/hf-blogs | chenglu | 2023-12-08T01:40:15Z | 161 | 0 | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"nlp",
"region:us"
]
| [
"text-classification"
]
| 2023-12-07T16:26:02Z | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- nlp
pretty_name: Hugging Face Blog Content
size_categories:
- n<1K
---
Hugging Face Blog Content.. | [
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|
lmg-anon/VNTL-v2-2k | lmg-anon | 2023-12-08T15:45:22Z | 158 | 0 | [
"region:us"
]
| null | 2023-12-07T23:09:07Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 89715749
num_examples: 17824
- name: val
num_bytes: 8953592
num_examples: 1766
download_size: 43301597
dataset_size: 98669341
---
# Dataset Card for "VNTL-v2-2k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
textminr/topic-labeling | textminr | 2023-12-07T19:25:54Z | 156 | 0 | [
"size_categories:n<1K",
"language:en",
"region:us"
]
| null | 2023-11-11T23:15:02Z | ---
language:
- en
size_categories:
- n<1K
configs:
- config_name: base
default: true
data_files:
- split: train
path: base/train.jsonl
- split: validation
path: base/validation.jsonl
- split: test
path: base/test.jsonl
--- | [
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|
haryoaw/COPAL | haryoaw | 2023-12-10T08:37:55Z | 155 | 5 | [
"task_categories:multiple-choice",
"size_categories:n<1K",
"language:id",
"license:cc-by-sa-4.0",
"arxiv:2311.01012",
"region:us"
]
| [
"multiple-choice"
]
| 2023-10-28T14:35:55Z | ---
license: cc-by-sa-4.0
task_categories:
- multiple-choice
language:
- id
size_categories:
- n<1K
configs:
- config_name: id
data_files:
- split: test
path: test_copal.csv
- split: test_colloquial
path: test_copal_colloquial.csv
---
## About COPAL-ID
COPAL-ID is an Indonesian causal commonsense reasoning dataset that captures local nuances. It provides a more natural portrayal of day-to-day causal reasoning within the Indonesian (especially Jakartan) cultural sphere. Professionally written and validatid from scratch by natives, COPAL-ID is more fluent and free from awkward phrases, unlike the translated XCOPA-ID.
COPAL-ID is a test set only, intended to be used as a benchmark.
For more details, please see [our paper](https://arxiv.org/abs/2311.01012).
### Local Nuances Categories
Our dataset consists of 3 subcategories: local-term, culture, and language reasoning.
- Local-term captures common knowledge for Indonesians that is most likely unknown or uncommon for non-natives, e.g., local foods, public figures, abbreviations, and other local concepts.
- Culture captures norms used in Indonesia.
- Language captures the reasoning for the language itself, for example, local idioms, figures of speech, as well as ambiguous words.
Specifically, the distribution of COPAL-ID across these categories is:
### Colloquial vs Standard Indonesian
In daily scenarios, almost no one in Indonesia uses purely formal Indonesian. Yet, many NLP datasets use formal Indonesian. This surely causes a domain mismatch with real-case settings. To accommodate this, COPAL-ID is written in two variations: Standard Indonesian and Colloquial Indonesian. If you use COPAL-ID to benchmark your model, we suggest testing on both variants. Generally, colloquial Indonesian is harder for models to handle.
## How to Use
```py
from datasets import load_dataset
copal_id_dataset = load_dataset('haryoaw/COPAL', 'id', subset='test')
copal_id_colloquial_dataset = load_dataset('haryoaw/COPAL', 'id', subset='test_colloquial')
```
## Data Collection and Human Performance
COPAL-ID was created through a rigorous data collection pipeline. Each example is written and checked by natives accustomed to Jakartan culture. Lastly, we have run a human benchmark performance test across native Jakartans, in which they achieved an average accuracy of ~95% in both formal and colloquial Indonesian variants, noting that this dataset is trivially easy for those familiar with the culture and local nuances of Indonesia, especially in Jakarta.
For more details, please see our paper.
## Limitation
Indonesia is a vast country with over 700+ languages and rich in culture. Therefore, it is impossible to pinpoint a singular culture. Our dataset is specifically designed to capture Jakarta's (the capital) local nuances. Expanding to different local nuances and languages across Indonesia is a future work.
## Cite Our Work
```
@article{wibowo2023copal,
title={COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances},
author={Wibowo, Haryo Akbarianto and Fuadi, Erland Hilman and Nityasya, Made Nindyatama and Prasojo, Radityo Eko and Aji, Alham Fikri},
journal={arXiv preprint arXiv:2311.01012},
year={2023}
}
``` | [
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|
tomas-gajarsky/cifar100-lt | tomas-gajarsky | 2023-12-10T21:57:46Z | 151 | 0 | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:cifar100",
"language:en",
"license:apache-2.0",
"region:us"
]
| [
"image-classification"
]
| 2023-05-05T15:43:58Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- cifar100
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-100
pretty_name: Cifar100-LT
dataset_info:
features:
- name: img
dtype: image
- name: fine_label
dtype:
class_label:
names:
'0': apple
'1': aquarium_fish
'2': baby
'3': bear
'4': beaver
'5': bed
'6': bee
'7': beetle
'8': bicycle
'9': bottle
'10': bowl
'11': boy
'12': bridge
'13': bus
'14': butterfly
'15': camel
'16': can
'17': castle
'18': caterpillar
'19': cattle
'20': chair
'21': chimpanzee
'22': clock
'23': cloud
'24': cockroach
'25': couch
'26': cra
'27': crocodile
'28': cup
'29': dinosaur
'30': dolphin
'31': elephant
'32': flatfish
'33': forest
'34': fox
'35': girl
'36': hamster
'37': house
'38': kangaroo
'39': keyboard
'40': lamp
'41': lawn_mower
'42': leopard
'43': lion
'44': lizard
'45': lobster
'46': man
'47': maple_tree
'48': motorcycle
'49': mountain
'50': mouse
'51': mushroom
'52': oak_tree
'53': orange
'54': orchid
'55': otter
'56': palm_tree
'57': pear
'58': pickup_truck
'59': pine_tree
'60': plain
'61': plate
'62': poppy
'63': porcupine
'64': possum
'65': rabbit
'66': raccoon
'67': ray
'68': road
'69': rocket
'70': rose
'71': sea
'72': seal
'73': shark
'74': shrew
'75': skunk
'76': skyscraper
'77': snail
'78': snake
'79': spider
'80': squirrel
'81': streetcar
'82': sunflower
'83': sweet_pepper
'84': table
'85': tank
'86': telephone
'87': television
'88': tiger
'89': tractor
'90': train
'91': trout
'92': tulip
'93': turtle
'94': wardrobe
'95': whale
'96': willow_tree
'97': wolf
'98': woman
'99': worm
- name: coarse_label
dtype:
class_label:
names:
'0': aquatic_mammals
'1': fish
'2': flowers
'3': food_containers
'4': fruit_and_vegetables
'5': household_electrical_devices
'6': household_furniture
'7': insects
'8': large_carnivores
'9': large_man-made_outdoor_things
'10': large_natural_outdoor_scenes
'11': large_omnivores_and_herbivores
'12': medium_mammals
'13': non-insect_invertebrates
'14': people
'15': reptiles
'16': small_mammals
'17': trees
'18': vehicles_1
'19': vehicles_2
config_name: cifar100
splits:
- name: train
- name: test
num_bytes: 22605519
num_examples: 10000
download_size: 169001437
---
# Dataset Card for CIFAR-100-LT (Long Tail)
## 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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Paper:** [Paper imbalanced example](https://openaccess.thecvf.com/content_CVPR_2019/papers/Cui_Class-Balanced_Loss_Based_on_Effective_Number_of_Samples_CVPR_2019_paper.pdf)
- **Leaderboard:** [r-10](https://paperswithcode.com/sota/long-tail-learning-on-cifar-100-lt-r-10) [r-100](https://paperswithcode.com/sota/long-tail-learning-on-cifar-100-lt-r-100)
### Dataset Summary
The CIFAR-100-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 100 distinct classes.
The number of samples within each class decreases exponentially with factors of 10 and 100.
The dataset includes 10,000 test images, with 100 images per class,
and fewer than 50,000 training images.
These 100 classes are further organized into 20 overarching superclasses.
Each image is assigned two labels: a fine label denoting the specific class,
and a coarse label representing the associated superclass.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/long-tail-learning-on-cifar-100-lt-r-100).
### Languages
English
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19,
'coarse_label': 11
}
```
### Data Fields
- `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `fine_label`: an `int` classification label with the following mapping:
`0`: apple
`1`: aquarium_fish
`2`: baby
`3`: bear
`4`: beaver
`5`: bed
`6`: bee
`7`: beetle
`8`: bicycle
`9`: bottle
`10`: bowl
`11`: boy
`12`: bridge
`13`: bus
`14`: butterfly
`15`: camel
`16`: can
`17`: castle
`18`: caterpillar
`19`: cattle
`20`: chair
`21`: chimpanzee
`22`: clock
`23`: cloud
`24`: cockroach
`25`: couch
`26`: cra
`27`: crocodile
`28`: cup
`29`: dinosaur
`30`: dolphin
`31`: elephant
`32`: flatfish
`33`: forest
`34`: fox
`35`: girl
`36`: hamster
`37`: house
`38`: kangaroo
`39`: keyboard
`40`: lamp
`41`: lawn_mower
`42`: leopard
`43`: lion
`44`: lizard
`45`: lobster
`46`: man
`47`: maple_tree
`48`: motorcycle
`49`: mountain
`50`: mouse
`51`: mushroom
`52`: oak_tree
`53`: orange
`54`: orchid
`55`: otter
`56`: palm_tree
`57`: pear
`58`: pickup_truck
`59`: pine_tree
`60`: plain
`61`: plate
`62`: poppy
`63`: porcupine
`64`: possum
`65`: rabbit
`66`: raccoon
`67`: ray
`68`: road
`69`: rocket
`70`: rose
`71`: sea
`72`: seal
`73`: shark
`74`: shrew
`75`: skunk
`76`: skyscraper
`77`: snail
`78`: snake
`79`: spider
`80`: squirrel
`81`: streetcar
`82`: sunflower
`83`: sweet_pepper
`84`: table
`85`: tank
`86`: telephone
`87`: television
`88`: tiger
`89`: tractor
`90`: train
`91`: trout
`92`: tulip
`93`: turtle
`94`: wardrobe
`95`: whale
`96`: willow_tree
`97`: wolf
`98`: woman
`99`: worm
- `coarse_label`: an `int` coarse classification label with following mapping:
`0`: aquatic_mammals
`1`: fish
`2`: flowers
`3`: food_containers
`4`: fruit_and_vegetables
`5`: household_electrical_devices
`6`: household_furniture
`7`: insects
`8`: large_carnivores
`9`: large_man-made_outdoor_things
`10`: large_natural_outdoor_scenes
`11`: large_omnivores_and_herbivores
`12`: medium_mammals
`13`: non-insect_invertebrates
`14`: people
`15`: reptiles
`16`: small_mammals
`17`: trees
`18`: vehicles_1
`19`: vehicles_2
### Data Splits
| name |train|test|
|----------|----:|---------:|
|cifar100|<50000| 10000|
### Licensing Information
Apache License 2.0
### Citation Information
```
@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchablani) and all contributors for adding the original balanced cifar100 dataset. | [
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|
cmu-mlsp/librispeech960-wavlm-large-km1000_asr_tokenized_final_fixed | cmu-mlsp | 2023-12-07T18:10:56Z | 150 | 0 | [
"region:us"
]
| null | 2023-12-06T23:49:59Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: validation_tts
path: data/validation_tts-*
- split: test
path: data/test-*
- split: test_tts
path: data/test_tts-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 5169983912
num_examples: 562482
- name: validation
num_bytes: 29571960
num_examples: 5406
- name: validation_tts
num_bytes: 14785980
num_examples: 2703
- name: test
num_bytes: 6104987
num_examples: 2620
- name: test_tts
num_bytes: 8664977
num_examples: 2620
download_size: 836237002
dataset_size: 5229111816
---
# Dataset Card for "librispeech960-wavlm-large-km1000_asr_tokenized_final_fixed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
shkang/sr-dataset | shkang | 2023-12-10T01:33:52Z | 150 | 0 | [
"region:us"
]
| null | 2023-12-10T01:33:47Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 4926476.914354644
num_examples: 746
- name: test
num_bytes: 548120.0856453559
num_examples: 83
download_size: 2197001
dataset_size: 5474597.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
aeromaki/20NewsGroups | aeromaki | 2023-12-08T14:39:35Z | 148 | 0 | [
"region:us"
]
| null | 2023-12-08T14:24:42Z | ---
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: id
dtype: int64
- name: text
dtype: string
- name: label
sequence: int64
splits:
- name: train
num_bytes: 11378604
num_examples: 8549
- name: validation
num_bytes: 1557031
num_examples: 1068
- name: test
num_bytes: 1320717
num_examples: 1047
download_size: 9030627
dataset_size: 14256352
---
# Dataset Card for "20NewsGroups"
```
{"label_text": ["#", "#.sci", "#.sci.electronics", "#.talk", "#.talk.religion", "#.talk.religion.misc", "#.sci.space", "#.comp", "#.comp.sys", "#.comp.sys.ibm", "#.comp.sys.ibm.pc", "#.comp.sys.ibm.pc.hardware", "#.rec", "#.rec.sport", "#.rec.sport.baseball", "#.soc", "#.soc.religion", "#.soc.religion.christian", "#.comp.windows", "#.comp.windows.x", "#.rec.sport.hockey", "#.rec.motorcycles", "#.comp.sys.mac", "#.comp.sys.mac.hardware", "#.comp.graphics", "#.talk.politics", "#.talk.politics.misc", "#.sci.med", "#.misc", "#.misc.forsale", "#.alt", "#.alt.atheism", "#.sci.crypt", "#.talk.politics.mideast", "#.talk.politics.guns", "#.comp.os", "#.comp.os.ms-windows", "#.comp.os.ms-windows.misc", "#.rec.autos"], "graph": [[0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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|
hippocrates/medQA_test | hippocrates | 2023-12-05T20:34:45Z | 146 | 0 | [
"region:us"
]
| null | 2023-10-03T12:12:56Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 14851133
num_examples: 10178
- name: valid
num_bytes: 1855041
num_examples: 1272
- name: test
num_bytes: 1883135
num_examples: 1273
download_size: 7379847
dataset_size: 18589309
---
# Dataset Card for "medQA_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
laion/220k-GPT4Vision-captions-from-LIVIS | laion | 2023-12-10T18:06:09Z | 142 | 12 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-21T19:43:59Z | ---
license: apache-2.0
---
# 220k-GPT4Vision-captions-from-LVIS
## by: Christoph Schuhmann, Peter Bevan, 21 Nov, 2023
---
This dataset comprises 220,000 captioned images from the LVIS dataset. The captions were generated by summarising the [LVIS-Instruct4V](https://huggingface.co/datasets/X2FD/LVIS-Instruct4V) dataset released by X2FD. The instructions are converted into captions using [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
---
### PROMPT
`"""<<SYS>> You are a highly intelligent, empathic, helpful, respectful, and honest assistant with high emotional intelligence.
Always answer as helpfully and honest as possible, while being safe. Your answers should not include any harmful, unethical, racist,
sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct.
If you don't know the answer to a question, please don't share false information. <</SYS>> DIALOGUE: {text} INSTRUCTIONS:
The previous DIALOGUE is a conversation between a chatbot and a user about an image. Please summarize all information and details about
the image the chatbot is talking about in DIALOGUE in one precise, very factual caption with as many details as you can extract from DIALOGUE.
Do not make up details about the image and stick strickly to the information in DIALOGUE. Only include factual, descriptive details about the image.
Start with the words "This image showcases":"""`
"This image showcases" was trimmed from the beginning of each caption upon generation.
---
# Citation
```bibtex
@misc{LAION_LVIS_220,
title = {220k-GPT4Vision-captions-from-LVIS},
author = {Christoph Schuhmann and Peter Bevan},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/datasets/laion/220k-GPT4Vision-captions-from-LIVIS}},
}
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|
ServiceNow/dssk_training_data | ServiceNow | 2023-12-07T22:18:11Z | 140 | 0 | [
"region:us"
]
| null | 2023-12-01T19:28:44Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: contexts_list
sequence: string
- name: titles_list
sequence: string
- name: useful_contexts
sequence: int64
- name: question
dtype: string
- name: answer
dtype: string
- name: sample_idx
dtype: int64
- name: dataset
dtype: string
splits:
- name: train
num_bytes: 9004487958.20289
num_examples: 1316190
- name: val
num_bytes: 953955198.6489936
num_examples: 140103
- name: test
num_bytes: 306924883.8764856
num_examples: 38633
download_size: 5517275094
dataset_size: 10265368040.72837
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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|
mattlc/toises_failure_cases_nov_2023 | mattlc | 2023-12-06T12:37:56Z | 137 | 0 | [
"region:us"
]
| null | 2023-12-04T23:43:21Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: type
dtype: string
splits:
- name: train
num_bytes: 399560349.0
num_examples: 129
download_size: 351359426
dataset_size: 399560349.0
---
# Dataset Card for "toises_failure_cases_nov_2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
ManuelAlv/test | ManuelAlv | 2023-12-08T05:30:59Z | 135 | 0 | [
"region:us"
]
| null | 2023-12-08T03:35:51Z | ---
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: input
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1332053
num_examples: 3700
- name: validation
num_bytes: 799362
num_examples: 2220
- name: test
num_bytes: 538180
num_examples: 1480
download_size: 1766713
dataset_size: 2669595
---
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|
joujiboi/japanese-anime-speech | joujiboi | 2023-12-05T14:23:15Z | 131 | 4 | [
"task_categories:automatic-speech-recognition",
"size_categories:10K<n<100K",
"language:ja",
"license:cc0-1.0",
"anime",
"japanese",
"日本語",
"nihongo",
"speech",
"audio-text",
"asr",
"whisper",
"voice",
"large-v3",
"region:us"
]
| [
"automatic-speech-recognition"
]
| 2023-11-07T13:53:40Z | ---
language:
- ja
license: cc0-1.0
size_categories:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
pretty_name: Japanese-Anime-Speech
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 3981327916.675
num_examples: 38325
download_size: 4168517599
dataset_size: 3981327916.675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- anime
- japanese
- 日本語
- nihongo
- speech
- audio-text
- asr
- whisper
- voice
- large-v3
---
# Japanese Anime Speech Dataset
**japanese-anime-speech** is an audio-text dataset designed for the training of automatic speech recognition models. The dataset is comprised of thousands of audio clips and their corresponding transcriptions from different visual novels.
The goal of this dataset is to enhance the proficiency of automatic speech recognition systems, such as OpenAI's [Whisper](https://huggingface.co/openai/whisper-large-v2), in accurately transcribing dialogue from anime and other similar Japanese media. This genre is characterized by unique linguistic features and speech patterns that diverge from conventional Japanese speech.
A list of all audio files and transcriptions are [**here**](https://huggingface.co/datasets/joujiboi/japanese-anime-speech/raw/main/audio_transcription_list.txt).
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Content Warning:</b> Please be advised that the majority of the audio in this dataset is sourced from visual novels and may include content that is not suitable for all audiences, such as suggestive sounds or mature topics. Efforts have been undertaken to minimise this content as much as possible. </p>
</div>
# Changelog
* V1 - This version contains **16,143** audio-text pairs from the visual novel `IxSHE Tell`. Some cleaning of the transcriptions has been done to get rid of unwanted characters at the start and end of lines, but I intend to do much more for the second version.
* V2 - The version contains **23,422** audio-text pairs from three different visual novels. Cleaning has been done to remove most nsfw lines, especially noises that aren't words. The audio is now in mp3 format, rather than wav. This version contains **32.6** hours of audio.
* **V3** - The dataset now contains **38,325** audio-text pairs from five different visual novels. Very thorough cleaning has been done to remove almost all nsfw or low-quality audio files. Transcriptions have been formatted to contain much fewer dramatised duplicated characters (for example 「ああああーーー」), and transcriptions have been made much more consistent (for example, using the same type of quotation mark). This version contains **52.5 hours** of audio.
# Dataset information
* **38,325** audio-text pairs
* **52.5 hours** of audio (OpenAI suggests a minimum of [5 hours](https://huggingface.co/blog/fine-tune-whisper) for productive [Whisper](https://huggingface.co/openai/whisper-large-v2) fine-tuning).
* **4.9s** average audio length.
* **All** transcriptions have been scraped directly from the game files of visual novels.
# To do
* [X] Create a dataset of over 10k items
* [X] Create a dataset of over 20k items
* [X] Compress the audio with minimal quality loss
* [X] Create a dataset of over 30k items
* [ ] Create more workflows for scraping audio from visual novels that use an engine other than Artemis
* [ ] Add audio from more visual novels
* [ ] Convert names in transcriptions to katakana?
# Use & Credit
This dataset is openly available for commercial or non-commercial use. Anyone is welcome to use this resource as they deem appropriate. However, the creator assumes no responsibility for the consequences of its use. While not mandatory, crediting this dataset with a hyperlink in any derivative work would be greatly appreciated.
I hope that by sharing this dataset, we (the open-source community) improve automatic speech recognition for anime content. | [
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|
Weni/zeroshot-3.0.3 | Weni | 2023-12-06T13:52:27Z | 131 | 0 | [
"region:us"
]
| null | 2023-11-30T19:36:48Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: true_class
dtype: string
splits:
- name: train
num_bytes: 31395403
num_examples: 32519
download_size: 12036754
dataset_size: 31395403
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
open-llm-leaderboard/details_ValiantLabs__ShiningValiant | open-llm-leaderboard | 2023-12-10T02:29:26Z | 130 | 0 | [
"region:us"
]
| null | 2023-09-02T11:23:43Z | ---
pretty_name: Evaluation run of ValiantLabs/ShiningValiant
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ValiantLabs/ShiningValiant](https://huggingface.co/ValiantLabs/ShiningValiant)\
\ 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 5 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_ValiantLabs__ShiningValiant\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-10T02:25:26.345827](https://huggingface.co/datasets/open-llm-leaderboard/details_ValiantLabs__ShiningValiant/blob/main/results_2023-12-10T02-25-26.345827.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\": 0.7110861444687467,\n\
\ \"acc_stderr\": 0.030063430253086363,\n \"acc_norm\": 0.7154441745417264,\n\
\ \"acc_norm_stderr\": 0.030639690759115483,\n \"mc1\": 0.5569155446756426,\n\
\ \"mc1_stderr\": 0.01738973034687711,\n \"mc2\": 0.7029226076594556,\n\
\ \"mc2_stderr\": 0.013335950631417065\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6552901023890785,\n \"acc_stderr\": 0.01388881628678211,\n\
\ \"acc_norm\": 0.7133105802047781,\n \"acc_norm_stderr\": 0.013214986329274776\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7438757219677355,\n\
\ \"acc_stderr\": 0.004355992090031012,\n \"acc_norm\": 0.9095797649870544,\n\
\ \"acc_norm_stderr\": 0.0028619676953189122\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.0315469804508223,\n\
\ \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.0315469804508223\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\
\ \"acc_stderr\": 0.041633319989322605,\n \"acc_norm\": 0.78,\n \
\ \"acc_norm_stderr\": 0.041633319989322605\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.0271342916287417,\n\
\ \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.0271342916287417\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8125,\n\
\ \"acc_stderr\": 0.032639560491693344,\n \"acc_norm\": 0.8125,\n\
\ \"acc_norm_stderr\": 0.032639560491693344\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956913,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956913\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n\
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\
\ \"acc_stderr\": 0.035506839891655796,\n \"acc_norm\": 0.6820809248554913,\n\
\ \"acc_norm_stderr\": 0.035506839891655796\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6893617021276596,\n \"acc_stderr\": 0.03025123757921317,\n\
\ \"acc_norm\": 0.6893617021276596,\n \"acc_norm_stderr\": 0.03025123757921317\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6482758620689655,\n \"acc_stderr\": 0.0397923663749741,\n\
\ \"acc_norm\": 0.6482758620689655,\n \"acc_norm_stderr\": 0.0397923663749741\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4656084656084656,\n \"acc_stderr\": 0.025690321762493848,\n \"\
acc_norm\": 0.4656084656084656,\n \"acc_norm_stderr\": 0.025690321762493848\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \
\ \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8258064516129032,\n \"acc_stderr\": 0.02157624818451459,\n \"\
acc_norm\": 0.8258064516129032,\n \"acc_norm_stderr\": 0.02157624818451459\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n \"\
acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.029311188674983127,\n\
\ \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.029311188674983127\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8787878787878788,\n \"acc_stderr\": 0.023253157951942084,\n \"\
acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.023253157951942084\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240528,\n\
\ \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240528\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.022939925418530616,\n\
\ \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.022939925418530616\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7773109243697479,\n \"acc_stderr\": 0.02702543349888238,\n \
\ \"acc_norm\": 0.7773109243697479,\n \"acc_norm_stderr\": 0.02702543349888238\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5165562913907285,\n \"acc_stderr\": 0.04080244185628972,\n \"\
acc_norm\": 0.5165562913907285,\n \"acc_norm_stderr\": 0.04080244185628972\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9100917431192661,\n \"acc_stderr\": 0.012264304540230444,\n \"\
acc_norm\": 0.9100917431192661,\n \"acc_norm_stderr\": 0.012264304540230444\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6342592592592593,\n \"acc_stderr\": 0.032847388576472056,\n \"\
acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.032847388576472056\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813905,\n \"\
acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813905\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.890295358649789,\n \"acc_stderr\": 0.02034340073486884,\n \
\ \"acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.02034340073486884\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8295964125560538,\n\
\ \"acc_stderr\": 0.025234593447136175,\n \"acc_norm\": 0.8295964125560538,\n\
\ \"acc_norm_stderr\": 0.025234593447136175\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n\
\ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807194,\n \"\
acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807194\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8404907975460123,\n \"acc_stderr\": 0.028767481725983854,\n\
\ \"acc_norm\": 0.8404907975460123,\n \"acc_norm_stderr\": 0.028767481725983854\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5267857142857143,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.036756688322331886,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.036756688322331886\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\
\ \"acc_stderr\": 0.017004368568132346,\n \"acc_norm\": 0.9273504273504274,\n\
\ \"acc_norm_stderr\": 0.017004368568132346\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8722860791826309,\n\
\ \"acc_stderr\": 0.011935626313999876,\n \"acc_norm\": 0.8722860791826309,\n\
\ \"acc_norm_stderr\": 0.011935626313999876\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7976878612716763,\n \"acc_stderr\": 0.02162807738019612,\n\
\ \"acc_norm\": 0.7976878612716763,\n \"acc_norm_stderr\": 0.02162807738019612\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6122905027932961,\n\
\ \"acc_stderr\": 0.016295332328155807,\n \"acc_norm\": 0.6122905027932961,\n\
\ \"acc_norm_stderr\": 0.016295332328155807\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7712418300653595,\n \"acc_stderr\": 0.024051029739912258,\n\
\ \"acc_norm\": 0.7712418300653595,\n \"acc_norm_stderr\": 0.024051029739912258\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.77491961414791,\n\
\ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\
\ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.02073635840806,\n\
\ \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.02073635840806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.599290780141844,\n \"acc_stderr\": 0.029233465745573096,\n \
\ \"acc_norm\": 0.599290780141844,\n \"acc_norm_stderr\": 0.029233465745573096\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5684485006518905,\n\
\ \"acc_stderr\": 0.012650007999463909,\n \"acc_norm\": 0.5684485006518905,\n\
\ \"acc_norm_stderr\": 0.012650007999463909\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.026799562024887667,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.026799562024887667\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7679738562091504,\n \"acc_stderr\": 0.01707737337785693,\n \
\ \"acc_norm\": 0.7679738562091504,\n \"acc_norm_stderr\": 0.01707737337785693\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940588,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940588\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8122448979591836,\n \"acc_stderr\": 0.02500025603954619,\n\
\ \"acc_norm\": 0.8122448979591836,\n \"acc_norm_stderr\": 0.02500025603954619\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8706467661691543,\n\
\ \"acc_stderr\": 0.02372983088101853,\n \"acc_norm\": 0.8706467661691543,\n\
\ \"acc_norm_stderr\": 0.02372983088101853\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646612,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646612\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5569155446756426,\n\
\ \"mc1_stderr\": 0.01738973034687711,\n \"mc2\": 0.7029226076594556,\n\
\ \"mc2_stderr\": 0.013335950631417065\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8421468034727704,\n \"acc_stderr\": 0.010247165248719763\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5466262319939348,\n \
\ \"acc_stderr\": 0.013712471049515446\n }\n}\n```"
repo_url: https://huggingface.co/ValiantLabs/ShiningValiant
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|arc:challenge|25_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|arc:challenge|25_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|arc:challenge|25_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|arc:challenge|25_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|arc:challenge|25_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|gsm8k|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hellaswag|10_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hellaswag|10_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hellaswag|10_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
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- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T14-51-34.241524.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-10-12T14-51-34.241524.parquet'
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- split: 2023_12_10T02_25_26.345827
path:
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T02-25-26.345827.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T02-25-26.345827.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T02-25-26.345827.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T02-25-26.345827.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T02-25-26.345827.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T02-25-26.345827.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T02-25-26.345827.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T02-25-26.345827.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T02-25-26.345827.parquet'
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- split: latest
path:
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- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T02-25-26.345827.parquet'
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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- split: 2023_09_22T22_26_27.274686
path:
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- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
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- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T14-51-34.241524.parquet'
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T14-51-34.241524.parquet'
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T14-51-34.241524.parquet'
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T14-10-33.371573.parquet'
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T14-51-34.241524.parquet'
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
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- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T14-10-33.371573.parquet'
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T14-51-34.241524.parquet'
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T11:23:20.160582.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T22-26-27.274686.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T14-10-33.371573.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T14-51-34.241524.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T11:23:20.160582.parquet'
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T14-10-33.371573.parquet'
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T14-51-34.241524.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T22-26-27.274686.parquet'
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T14-10-33.371573.parquet'
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T14-51-34.241524.parquet'
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T11:23:20.160582.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T22-26-27.274686.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T14-10-33.371573.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T14-51-34.241524.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T22-26-27.274686.parquet'
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T14-10-33.371573.parquet'
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
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- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
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- split: 2023_10_12T14_51_34.241524
path:
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- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
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- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T02-25-26.345827.parquet'
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path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-02T11:23:20.160582.parquet'
- split: 2023_09_22T22_26_27.274686
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T22-26-27.274686.parquet'
- split: 2023_10_12T14_10_33.371573
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-12T14-10-33.371573.parquet'
- split: 2023_10_12T14_51_34.241524
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-12T14-51-34.241524.parquet'
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T02-25-26.345827.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_10T02_25_26.345827
path:
- '**/details_harness|winogrande|5_2023-12-10T02-25-26.345827.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-10T02-25-26.345827.parquet'
- config_name: results
data_files:
- split: 2023_09_02T11_23_20.160582
path:
- results_2023-09-02T11:23:20.160582.parquet
- split: 2023_09_22T22_26_27.274686
path:
- results_2023-09-22T22-26-27.274686.parquet
- split: 2023_10_12T14_10_33.371573
path:
- results_2023-10-12T14-10-33.371573.parquet
- split: 2023_10_12T14_51_34.241524
path:
- results_2023-10-12T14-51-34.241524.parquet
- split: 2023_12_10T02_25_26.345827
path:
- results_2023-12-10T02-25-26.345827.parquet
- split: latest
path:
- results_2023-12-10T02-25-26.345827.parquet
---
# Dataset Card for Evaluation run of ValiantLabs/ShiningValiant
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ValiantLabs/ShiningValiant
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ValiantLabs/ShiningValiant](https://huggingface.co/ValiantLabs/ShiningValiant) 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 5 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_ValiantLabs__ShiningValiant",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-10T02:25:26.345827](https://huggingface.co/datasets/open-llm-leaderboard/details_ValiantLabs__ShiningValiant/blob/main/results_2023-12-10T02-25-26.345827.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": 0.7110861444687467,
"acc_stderr": 0.030063430253086363,
"acc_norm": 0.7154441745417264,
"acc_norm_stderr": 0.030639690759115483,
"mc1": 0.5569155446756426,
"mc1_stderr": 0.01738973034687711,
"mc2": 0.7029226076594556,
"mc2_stderr": 0.013335950631417065
},
"harness|arc:challenge|25": {
"acc": 0.6552901023890785,
"acc_stderr": 0.01388881628678211,
"acc_norm": 0.7133105802047781,
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```
### 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] | [
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|
lnwang/retrieval_qa | lnwang | 2023-12-08T07:37:32Z | 130 | 4 | [
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"language:ja",
"language:es",
"language:de",
"language:ru",
"license:apache-2.0",
"art",
"region:us"
]
| null | 2023-11-24T03:26:11Z | ---
language:
- en
- zh
- ja
- es
- de
- ru
license: apache-2.0
size_categories:
- 1K<n<10K
dataset_info:
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num_examples: 196
download_size: 0
dataset_size: 201205
configs:
- config_name: de
data_files:
- split: test
path: de/test-*
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: en
data_files:
- split: test
path: en/test-*
- config_name: es
data_files:
- split: test
path: es/test-*
- config_name: ja
data_files:
- split: test
path: ja/test-*
- config_name: ru
data_files:
- split: test
path: ru/test-*
- config_name: zh_cn
data_files:
- split: test
path: zh_cn/test-*
- config_name: zh_tw
data_files:
- split: test
path: zh_tw/test-*
tags:
- art
---
# Retrieval_QA: A Simple Multilingual Benchmark For Retrieval Encoder Models
<!-- Provide a quick summary of the dataset. -->
The purpose of this dataset is to provide a simple and easy-to-use benchmark for retrieval encoder models, which helps researchers quickly select the most effective retrieval encoder for text extraction and achieve optimal results in subsequent retrieval tasks such as retrieval-augmented-generation (RAG). The dataset contains multiple document-question pairs, where each document is a short text about the history, culture, or other information of a country or region, and each question is a query relevant to the content of the corresponding document.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
Users may select a retrieval encoder model to encode each document and query into corresponding embeddings, and then use vector matching methods such as FAISS to identify the most relevant documents for each query as regression results.
+ **Curated by**: <a href='https://wln20.github.io'>Luning Wang</a>
+ **Language(s)**: English, Chinese(Simplified, Traditional), Japanse, Spanish, German, Russian
+ **License**: Apache-2.0
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/wln20/Retrieval_QA
- **Paper:** TBD
- **Demo:** TBD
## Uses
The dataset is available on 🤗 Huggingface, you can conveniently use it in python with 🤗 Datasets:
```python
from datasets import load_dataset
dataset_en = load_dataset('lnwang/retrieval_qa', name='en')
# dataset_zh_cn = load_dataset('lnwang/retrieval_qa', name='zh_cn')
# dataset_zh_tw = load_dataset('lnwang/retrieval_qa', name='zh_tw')
```
Now we support three languages: English(en), Simplified-Chinese(zh_cn), Traditional-Chinese(zh_tw), Japanese(ja), Spanish(es), German(de), Russian(ru). You can specify the `name` argument in `load_dataset()` to get the corresponding subset.
For more usages, please follow the examples in the github repository of this project.
## Dataset Creation
The raw data was generated by GPT-3.5-turbo, using carefully designed prompts by human. The data was also cleaned to remove controversial information. | [
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|
Jason773/w1024_part_1 | Jason773 | 2023-12-07T06:13:16Z | 114 | 0 | [
"region:us"
]
| null | 2023-12-07T06:11:05Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: target
dtype: string
- name: source
dtype: string
- name: target_img
dtype: image
- name: source_img
dtype: image
splits:
- name: train
num_bytes: 3664854161.6
num_examples: 14984
download_size: 3717646259
dataset_size: 3664854161.6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
yashraizada/yelp-open-dataset-business | yashraizada | 2023-12-06T15:37:11Z | 108 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-06T15:34:59Z | ---
license: apache-2.0
---
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|
cmaldona/Generalization-MultiClass-CLINC150-ROSTD | cmaldona | 2023-12-09T18:52:24Z | 107 | 0 | [
"task_categories:text-classification",
"language:en",
"license:openrail",
"region:us"
]
| [
"text-classification"
]
| 2023-09-05T21:35:36Z | ---
name: generalization-test
version: 1.0.0
description: Merge between 3 datasets.
configs:
- config_name: clinc150
default: true
data_files:
- split: train
path: "train_clinc150.csv"
- split: validation
path: "validation_clinc150.csv"
- split: test
path: "test_clinc150.csv"
- config_name: rostd+
data_files:
- split: train
path: "train_rostd+.csv"
- split: validation
path: "val_rostd+.csv"
- split: test
path: "test_rostd+.csv"
license: openrail
task_categories:
- text-classification
language:
- en
---
This dataset merge 3 datasets and have two setup for experiments in generalisation for multi-class clasificacitino task.
* ID, near-OOD, covariate-shitf: [CLINC150](https://github.com/clinc/oos-eval)
* ID, near-OOD, covariate-shitf: [ROSTD+OOD](https://github.com/vgtomahawk/LR_GC_OOD) (fbreleasecoarse version)
* far-OOD Validation: [SST2](https://huggingface.co/datasets/sst2)
* far-OOD Test: [News Category](https://www.kaggle.com/datasets/rmisra/news-category-dataset?resource=download) (v3) | [
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|
nicholasKluge/instruct-aira-dataset | nicholasKluge | 2023-12-10T14:27:11Z | 105 | 4 | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:pt",
"language:en",
"language:es",
"license:apache-2.0",
"alignment",
"instruction",
"chat",
"region:us"
]
| [
"conversational",
"text-generation"
]
| 2023-06-07T17:09:55Z | ---
language:
- pt
- en
- es
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- conversational
- text-generation
pretty_name: Instruct-Aira Dataset
tags:
- alignment
- instruction
- chat
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: portuguese
num_bytes: 52023662
num_examples: 40945
- name: english
num_bytes: 47254561
num_examples: 41762
- name: spanish
num_bytes: 53176782
num_examples: 40946
download_size: 85078532
dataset_size: 152455005
configs:
- config_name: default
data_files:
- split: portuguese
path: data/portuguese-*
- split: english
path: data/english-*
- split: spanish
path: data/spanish-*
---
# Dataset (`Instruct-Aira Dataset`)
## Overview
This dataset contains a collection of demonstrations on how to answer questions and follow instructions. We used prompts from the [`synthetic-instruct-gptj-pairwise`](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) dataset, the [`databricks_dolly_15k`](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) dataset, and the [`instruction-dataset`](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) dataset, to create an instruction-tuning dataset, where the completions were generated by already tuned models (ChatGPT, LLama 2, Open-Assistant, etc). The dataset is available in both Portuguese, English, and Spanish.
## Dataset Details
- **Dataset Name:** Instruct-Aira Dataset
- **Language:** Portuguese, English, Spanish
- **Total Size:** Over 40,000 demonstrations
## Contents
The dataset consists of data frames with the following columns:
- **Prompt:** The initial text or question provided to the model.
- **Completion:** The demonstration of a generated completion or response for the given prompt.
```python
{
"prompt":"What is the capital of Brazil?",
"completion": "The capital of Brazil is Brasília."
}
```
All `prompt + completion` examples are less than 400 tokens (measured using the `GPT-2` and `BLOOM` tokenizers).
## Use Cases
`Instruct-Aira Dataset` can be utilized for various natural language processing tasks, including but not limited to:
- Language generation.
- Question-answering systems.
- Chatbot development.
- Evaluation of language models.
- AI ethics research.
- Alignment research.
## How to use
Available splits are `english` and `portuguese`.
```python
from datasets import load_dataset
dataset = load_dataset("nicholasKluge/instruct-aira-dataset")
```
## Disclaimer
This dataset is provided as is, without any warranty or guarantee of its accuracy or suitability for any purpose. The creators and contributors of this dataset are not liable for any damages or losses arising from its use. Please review and comply with the licenses and terms of the original datasets before use. | [
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|
philikai/SQL_Spider_DDL | philikai | 2023-12-08T12:05:09Z | 98 | 1 | [
"size_categories:1K<n<10K",
"license:cc-by-sa-4.0",
"region:us"
]
| null | 2023-10-26T12:44:18Z | ---
license: cc-by-sa-4.0
dataset_info:
features:
- name: DDL_schema
dtype: string
- name: query
dtype: string
- name: question
dtype: string
- name: db_id
dtype: string
splits:
- name: train
num_bytes: 15862354
num_examples: 8659
- name: validation
num_bytes: 1328365
num_examples: 1034
download_size: 621170
dataset_size: 17190719
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
size_categories:
- 1K<n<10K
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|
didsr/msynth | didsr | 2023-12-07T18:58:27Z | 96 | 3 | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:10K<n<100K",
"license:cc0-1.0",
"medical",
"arxiv:2310.18494",
"region:us"
]
| [
"image-classification",
"image-segmentation"
]
| 2023-10-26T21:32:23Z | ---
license: cc0-1.0
task_categories:
- image-classification
- image-segmentation
tags:
- medical
pretty_name: M-SYNTH
size_categories:
- 10K<n<100K
---
# M-SYNTH
<!-- Provide a quick summary of the dataset. -->
M-SYNTH is a synthetic digital mammography (DM) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://github.com/DIDSR/VICTRE) toolkit.
## Dataset Details
The dataset has the following characteristics:
* Breast density: dense, heterogeneously dense, scattered, fatty
* Mass radius (mm): 5.00, 7.00, 9.00
* Mass density: 1.0, 1.06, 1.1 (ratio of radiodensity of the mass to that of fibroglandular tissue)
* Relative dose: 20%, 40%, 60%, 80%, 100% of the clinically recommended dose for each density
<p align="center">
<img src='https://raw.githubusercontent.com/DIDSR/msynth-release/main/images/examples.png' width='700'>
</p>
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [Elena Sizikova](https://esizikova.github.io/), [Niloufar Saharkhiz](https://www.linkedin.com/in/niloufar-saharkhiz/), [Diksha Sharma](https://www.linkedin.com/in/diksha-sharma-6059977/), [Miguel Lago](https://www.linkedin.com/in/milaan/), [Berkman Sahiner](https://www.linkedin.com/in/berkman-sahiner-6aa9a919/), [Jana Gut Delfino](https://www.linkedin.com/in/janadelfino/), [Aldo Badano](https://www.linkedin.com/in/aldobadano/)
- **License:** Creative Commons 1.0 Universal License (CC0)
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Code:** [https://github.com/DIDSR/msynth-release](https://github.com/DIDSR/msynth-release)
- **Paper:** [https://arxiv.org/pdf/2310.18494.pdf](https://arxiv.org/pdf/2310.18494.pdf)
- **Demo:** [https://github.com/DIDSR/msynth-release/tree/master/examples](https://github.com/DIDSR/msynth-release/tree/master/examples)
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
M-SYNTH is intended to facilitate testing of AI with pre-computed synthetic mammography data.
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
M-SYNTH can be used to evaluate the effect of mass size and density, breast density, and dose on AI performance in lesion detection.
M-SYNTH can be used to either train or test pre-trained AI models.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
M-SYNTH cannot be used in lieu of real patient examples to make performance determinations.
## 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. -->
M-SYNTH is organized into a directory structure that indicates the parameters. The folder
```
device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[DOSE]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM/P2_[LESION_SIZE]_[BREAST_DENSITY].8337609.[PHANTOM_FILE_ID]/[PHANTOM_FILEID]/
```
contains image files imaged with the specified parameters. Note that only examples with odd PHANTOM_FILEID contain lesions, others do not.
```
$ tree data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM/P2_5.0_hetero.8337609.1/1/
data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM/P2_5.0_hetero.8337609.1/1/
├── DICOM_dm
│ └── 000.dcm
├── projection_DM1.loc
├── projection_DM1.mhd
└── projection_DM1.raw
```
Each folder contains mammogram data that can be read from .raw format (.mhd contains supporting data), or DICOM (.dcm) format.
Coordinates of lesions can be found in .loc files. Segmentations are stored in .raw format and can be found in data/segmentation_masks/* .
See [Github](https://github.com/DIDSR/msynth-release/tree/main/code) for examples of how to access the files, and [examples](https://github.com/DIDSR/msynth-release/tree/main/examples) for code to load each type of file.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Simulation-based testing is constrained to the parameter variability represented in the object model and the acquisition system.
There is a risk of misjudging model performance if the simulated examples do not capture the variability in real patients. Please
see the paper for a full discussion of biases, risks, and limitations.
## How to use it
The msynth dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`.
The msynth dataset has three configurations: 1) device_data, 2) segmentation_mask, and 3) metadata
You can load and iterate through the dataset using the configurations with the following lines of code:
```python
from datasets import load_dataset
ds = load_dataset("didsr/msynth", 'device_data') # For device data for all breast density, mass redius, mass density, and relative dose, change configuration to 'segmentation_mask' and 'metadata' to load the segmentation masks and bound information
print(ds_data["device_data"])
# A sample data instance
{'Raw': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1\\SIM\\P2_5.0_fatty.8336179.1\\1\\projection_DM1.raw',
'mhd': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\projection_DM1.mhd',
'loc': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\projection_DM1.loc',
'dcm': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\DICOM_dm\\000.dcm',
'density': 'fatty',
'mass_radius': 5.0}
```
Msynth dataset can also be loaded using custom breast density, mass redius, mass density, and relative dose information
```python
from datasets import load_dataset
# Dataset properties. change to 'all' to include all the values of breast density, mass redius, mass density, and relative dose information
config_kwargs = {
"lesion_density": ["1.0"],
"dose": ["20%"],
"density": ["fatty"],
"size": ["5.0"]
}
# Loading device data
ds_data = load_dataset("didsr/msynth", 'device_data', **config_kwargs)
# Loading segmentation-mask
ds_seg = load_dataset("didsr/msynth", 'segmentation_mask', **config_kwargs)
```
The meta data can also be loaded using the datasets API. An example of using metadata is given in **Demo:** [https://github.com/DIDSR/msynth-release/tree/master/examples](https://github.com/DIDSR/msynth-release/tree/master/examples)
```python
from datasets import load_dataset
# Loading metadata
ds_meta = load_dataset("didsr/msynth", 'metadata')
# A sample data instance
ds_meta['metadata'][0]
# Output
{'fatty': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_fatty.npy',
'dense': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_dense.npy',
'hetero': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_hetero.npy',
'scattered': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_scattered.npy'}
```
## Citation
```
@article{sizikova2023knowledge,
title={Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses},
author={Sizikova, Elena and Saharkhiz, Niloufar and Sharma, Diksha and Lago, Miguel and Sahiner, Berkman and Delfino, Jana G. and Badano, Aldo},
journal={Advances in Neural Information Processing Systems},
volume={},
pages={},
year={2023}
}
```
## Related Links
1. [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://www.fda.gov/medical-devices/science-and-research-medical-devices/victre-silico-breast-imaging-pipeline).
2. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Devices](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).
3. A. Badano, C. G. Graff, A. Badal, D. Sharma, R. Zeng, F. W. Samuelson, S. Glick, K. J. Myers. [Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial](http://dx.doi.org/10.1001/jamanetworkopen.2018.5474). JAMA Network Open 2018.
4. A. Badano, M. Lago, E. Sizikova, J. G. Delfino, S. Guan, M. A. Anastasio, B. Sahiner. [The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts.](http://dx.doi.org/10.1088/2516-1091/ad04c0) Progress in Biomedical Engineering 2023.
5. E. Sizikova, N. Saharkhiz, D. Sharma, M. Lago, B. Sahiner, J. G. Delfino, A. Badano. [Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI](https://github.com/DIDSR/msynth-release). NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI. | [
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|
coastalcph/fm-updates-alpaca-7b | coastalcph | 2023-12-06T09:11:02Z | 94 | 0 | [
"region:us"
]
| null | 2023-11-08T13:29:51Z | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: query
struct:
- name: label
dtype: string
- name: objects
list:
- name: aliases
sequence: string
- name: label
dtype: string
- name: qid
dtype: string
- name: qid
dtype: string
- name: rel_id
dtype: string
- name: relation
dtype: string
- name: prediction
struct:
- name: predictions
list:
- name: answer
dtype: string
- name: first_token_probability
dtype: float64
- name: per_token_probability
sequence: float64
- name: perplexity
dtype: float64
- name: query
dtype: string
- name: f1
dtype: float64
- name: relation
dtype: string
- name: type
dtype: string
- name: original_answer
dtype: string
- name: updates
sequence: string
splits:
- name: test
num_bytes: 427781.57826343767
num_examples: 1098
- name: validation
num_bytes: 46752.08505611341
num_examples: 120
download_size: 301820
dataset_size: 474533.6633195511
---
# Dataset Card for "fm-updates-alpaca-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
abishekcodes/self_qna | abishekcodes | 2023-12-10T13:14:30Z | 92 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-02T19:33:49Z | ---
license: mit
---
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|
dyxohjl666/CocoScisum_ACL | dyxohjl666 | 2023-12-06T16:37:19Z | 92 | 0 | [
"region:us"
]
| null | 2023-12-03T07:53:21Z | ---
configs:
- config_name: default
data_files:
- split: coco
path: coco/*json
- split: kw
path: kw/*.json
- split: len
path: len/*.json
- split: vanilla
path: vanilla/*.json
dataset_info:
features:
- name: url
dtype: string
- name: text
dtype: string
- name: keywords
dtype: string
- name: length
dtype: string
- name: summary
dtype: string
---
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|
euisuh15/poison-cwe | euisuh15 | 2023-12-06T20:02:17Z | 89 | 0 | [
"region:us"
]
| null | 2023-12-06T17:10:03Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test1
path: data/test1-*
- split: test2
path: data/test2-*
- split: val
path: data/val-*
- split: new_test1
path: data/new_test1-*
- split: new_test2
path: data/new_test2-*
dataset_info:
features:
- name: file_change_id
dtype: int64
- name: method_change_id
dtype: int64
- name: code
dtype: string
- name: name
dtype: string
- name: cwe_id
dtype: string
- name: cve_id
dtype: string
- name: before_change
dtype: bool
- name: index
dtype: int64
- name: index_grouped
dtype: string
- name: count
dtype: float64
- name: type
dtype: string
- name: output
dtype: string
- name: input
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 4045827
num_examples: 1798
- name: test1
num_bytes: 539359
num_examples: 226
- name: test2
num_bytes: 745301
num_examples: 308
- name: val
num_bytes: 339243
num_examples: 146
- name: new_test1
num_bytes: 66028
num_examples: 20
- name: new_test2
num_bytes: 35658
num_examples: 20
download_size: 73465
dataset_size: 5771416
---
# Dataset Card for "poison-cwe"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
davidfant/rapidapi-example-responses | davidfant | 2023-12-10T11:16:52Z | 88 | 0 | [
"region:us"
]
| null | 2023-12-09T18:25:26Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: api_name
dtype: string
- name: api_description
dtype: string
- name: api_score
dtype: float64
- name: endpoint_name
dtype: string
- name: endpoint_description
dtype: string
- name: response_status_code
dtype: int64
- name: response_summary
dtype: string
- name: response_json
dtype: string
- name: response_json_schema
dtype: string
splits:
- name: train
num_bytes: 115936364
num_examples: 28059
download_size: 27933521
dataset_size: 115936364
---
# Dataset Card for "rapidapi-example-responses"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
open-llm-leaderboard/details_uukuguy__speechless-llama2-13b | open-llm-leaderboard | 2023-12-09T16:52:57Z | 87 | 0 | [
"region:us"
]
| null | 2023-09-02T03:45:47Z | ---
pretty_name: Evaluation run of uukuguy/speechless-llama2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-llama2-13b](https://huggingface.co/uukuguy/speechless-llama2-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 5 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_uukuguy__speechless-llama2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-09T16:49:00.911665](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-llama2-13b/blob/main/results_2023-12-09T16-49-00.911665.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\": 0.5860683187201721,\n\
\ \"acc_stderr\": 0.033316954491979946,\n \"acc_norm\": 0.5913412721401082,\n\
\ \"acc_norm_stderr\": 0.0340008049750402,\n \"mc1\": 0.3880048959608323,\n\
\ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5565985023189125,\n\
\ \"mc2_stderr\": 0.015435738665954496\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5784982935153583,\n \"acc_stderr\": 0.014430197069326023,\n\
\ \"acc_norm\": 0.6203071672354948,\n \"acc_norm_stderr\": 0.014182119866974872\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6212905795658236,\n\
\ \"acc_stderr\": 0.004840742206718088,\n \"acc_norm\": 0.8181637124078869,\n\
\ \"acc_norm_stderr\": 0.0038492126228151643\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5481481481481482,\n\
\ \"acc_stderr\": 0.042992689054808644,\n \"acc_norm\": 0.5481481481481482,\n\
\ \"acc_norm_stderr\": 0.042992689054808644\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.0403356566784832,\n\
\ \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.0403356566784832\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.630188679245283,\n \"acc_stderr\": 0.02971142188010793,\n\
\ \"acc_norm\": 0.630188679245283,\n \"acc_norm_stderr\": 0.02971142188010793\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6458333333333334,\n\
\ \"acc_stderr\": 0.039994111357535424,\n \"acc_norm\": 0.6458333333333334,\n\
\ \"acc_norm_stderr\": 0.039994111357535424\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n \
\ },\n \"harness|hendrycksTest-college_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\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5491329479768786,\n\
\ \"acc_stderr\": 0.037940126746970296,\n \"acc_norm\": 0.5491329479768786,\n\
\ \"acc_norm_stderr\": 0.037940126746970296\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.04655010411319616,\n\
\ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.04655010411319616\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.49361702127659574,\n \"acc_stderr\": 0.032683358999363366,\n\
\ \"acc_norm\": 0.49361702127659574,\n \"acc_norm_stderr\": 0.032683358999363366\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n\
\ \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n\
\ \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192118,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192118\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159798,\n \"\
acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159798\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n\
\ \"acc_stderr\": 0.04325506042017087,\n \"acc_norm\": 0.373015873015873,\n\
\ \"acc_norm_stderr\": 0.04325506042017087\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6838709677419355,\n\
\ \"acc_stderr\": 0.026450874489042764,\n \"acc_norm\": 0.6838709677419355,\n\
\ \"acc_norm_stderr\": 0.026450874489042764\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\"\
: 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.03567969772268049,\n\
\ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.03567969772268049\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932026,\n \"\
acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932026\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.02649905770139744,\n\
\ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.02649905770139744\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6230769230769231,\n \"acc_stderr\": 0.024570975364225995,\n\
\ \"acc_norm\": 0.6230769230769231,\n \"acc_norm_stderr\": 0.024570975364225995\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606648,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606648\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5966386554621849,\n \"acc_stderr\": 0.031866081214088314,\n\
\ \"acc_norm\": 0.5966386554621849,\n \"acc_norm_stderr\": 0.031866081214088314\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\
acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7908256880733945,\n \"acc_stderr\": 0.01743793717334323,\n \"\
acc_norm\": 0.7908256880733945,\n \"acc_norm_stderr\": 0.01743793717334323\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.39814814814814814,\n \"acc_stderr\": 0.033384734032074016,\n \"\
acc_norm\": 0.39814814814814814,\n \"acc_norm_stderr\": 0.033384734032074016\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.02812597226565438,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.02812597226565438\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \
\ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.031024411740572206,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.031024411740572206\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n\
\ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7272727272727273,\n \"acc_stderr\": 0.04065578140908706,\n \"\
acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04065578140908706\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n\
\ \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n\
\ \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.4017857142857143,\n\
\ \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.02441494730454368,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.02441494730454368\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7624521072796935,\n\
\ \"acc_stderr\": 0.015218733046150191,\n \"acc_norm\": 0.7624521072796935,\n\
\ \"acc_norm_stderr\": 0.015218733046150191\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.025722802200895803,\n\
\ \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.025722802200895803\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41899441340782123,\n\
\ \"acc_stderr\": 0.01650157930686168,\n \"acc_norm\": 0.41899441340782123,\n\
\ \"acc_norm_stderr\": 0.01650157930686168\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6568627450980392,\n \"acc_stderr\": 0.027184498909941616,\n\
\ \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.027184498909941616\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6591639871382636,\n\
\ \"acc_stderr\": 0.026920841260776165,\n \"acc_norm\": 0.6591639871382636,\n\
\ \"acc_norm_stderr\": 0.026920841260776165\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.026462487777001872,\n\
\ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.026462487777001872\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4367666232073012,\n\
\ \"acc_stderr\": 0.012667701919603654,\n \"acc_norm\": 0.4367666232073012,\n\
\ \"acc_norm_stderr\": 0.012667701919603654\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5808823529411765,\n \"acc_stderr\": 0.029972807170464622,\n\
\ \"acc_norm\": 0.5808823529411765,\n \"acc_norm_stderr\": 0.029972807170464622\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5669934640522876,\n \"acc_stderr\": 0.02004544247332422,\n \
\ \"acc_norm\": 0.5669934640522876,\n \"acc_norm_stderr\": 0.02004544247332422\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6775510204081633,\n \"acc_stderr\": 0.029923100563683906,\n\
\ \"acc_norm\": 0.6775510204081633,\n \"acc_norm_stderr\": 0.029923100563683906\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7562189054726368,\n\
\ \"acc_stderr\": 0.030360490154014645,\n \"acc_norm\": 0.7562189054726368,\n\
\ \"acc_norm_stderr\": 0.030360490154014645\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4879518072289157,\n\
\ \"acc_stderr\": 0.03891364495835821,\n \"acc_norm\": 0.4879518072289157,\n\
\ \"acc_norm_stderr\": 0.03891364495835821\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.031885780176863984,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.031885780176863984\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3880048959608323,\n\
\ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5565985023189125,\n\
\ \"mc2_stderr\": 0.015435738665954496\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7600631412786109,\n \"acc_stderr\": 0.012002078629485739\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.33965125094768767,\n \
\ \"acc_stderr\": 0.013045045067665269\n }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-llama2-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|arc:challenge|25_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|arc:challenge|25_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|arc:challenge|25_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|arc:challenge|25_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T22_09_28.481990
path:
- '**/details_harness|drop|3_2023-10-15T22-09-28.481990.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T22-09-28.481990.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T22_09_28.481990
path:
- '**/details_harness|gsm8k|5_2023-10-15T22-09-28.481990.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|gsm8k|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_02T03_45_23.206143
path:
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-49-00.911665.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-49-00.911665.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-49-00.911665.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-49-00.911665.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-49-00.911665.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-02T03:45:23.206143.parquet'
- split: 2023_09_02T15_58_18.299905
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-02T15:58:18.299905.parquet'
- split: 2023_09_12T13_57_07.476950
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T13-57-07.476950.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T16-49-00.911665.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T22_09_28.481990
path:
- '**/details_harness|winogrande|5_2023-10-15T22-09-28.481990.parquet'
- split: 2023_12_09T16_49_00.911665
path:
- '**/details_harness|winogrande|5_2023-12-09T16-49-00.911665.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-09T16-49-00.911665.parquet'
- config_name: results
data_files:
- split: 2023_09_02T03_45_23.206143
path:
- results_2023-09-02T03:45:23.206143.parquet
- split: 2023_09_02T15_58_18.299905
path:
- results_2023-09-02T15:58:18.299905.parquet
- split: 2023_09_12T13_57_07.476950
path:
- results_2023-09-12T13-57-07.476950.parquet
- split: 2023_10_15T22_09_28.481990
path:
- results_2023-10-15T22-09-28.481990.parquet
- split: 2023_12_09T16_49_00.911665
path:
- results_2023-12-09T16-49-00.911665.parquet
- split: latest
path:
- results_2023-12-09T16-49-00.911665.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-llama2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-llama2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [uukuguy/speechless-llama2-13b](https://huggingface.co/uukuguy/speechless-llama2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 5 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_uukuguy__speechless-llama2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-09T16:49:00.911665](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-llama2-13b/blob/main/results_2023-12-09T16-49-00.911665.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc2_stderr": 0.015435738665954496
},
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},
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
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"acc_stderr": 0.013045045067665269
}
}
```
### 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] | [
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|
jakartaresearch/id-review-gen | jakartaresearch | 2023-12-10T08:29:23Z | 87 | 0 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:id",
"license:apache-2.0",
"product review",
"review",
"region:us"
]
| [
"text-generation"
]
| 2023-12-09T23:49:19Z | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- id
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: id-review-gen
tags:
- product review
- review
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 13966269
num_examples: 114992
download_size: 13384578
dataset_size: 13966269
---
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|
locuslab/TOFU | locuslab | 2023-12-08T23:16:03Z | 85 | 2 | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"unlearning",
"question answering",
"TOFU",
"NLP",
"LLM",
"region:us"
]
| [
"question-answering"
]
| 2023-11-14T22:25:09Z | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: TOFU
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- unlearning
- question answering
- TOFU
- NLP
- LLM
task_categories:
- question-answering
task_ids:
- closed-domain-qa
configs:
- config_name: full
data_files: "full.json"
default: true
- config_name: forget01
data_files: "forget01.json"
- config_name: forget05
data_files: "forget05.json"
- config_name: forget10
data_files: "forget10.json"
- config_name: retain90
data_files: "retain90.json"
- config_name: retain95
data_files: "retain95.json"
- config_name: retain99
data_files: "retain99.json"
- config_name: world_facts
data_files: "world_facts.json"
- config_name: real_authors
data_files: "real_authors.json"
- config_name: forget01_perturbed
data_files: "forget01_perturbed.json"
- config_name: forget05_perturbed
data_files: "forget05_perturbed.json"
- config_name: forget10_perturbed
data_files: "forget10_perturbed.json"
- config_name: retain_perturbed
data_files: "retain_perturbed.json"
---
# TOFU: Task of Fictitious Unlearning 🍢
The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set.
## Applicability 🚀
The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 model, but can be easily adapted to other models.
## Loading the Dataset
To load the dataset, use the following code:
```python
from datasets import load_dataset
dataset = load_dataset("locuslab/TOFU", "full")
```
### Available forget sets are:
- `forget01`: Forgetting 1% of the original dataset, all entries correspond to a single author.
- `forget05`: Forgetting 5% of the original dataset, all entries correspond to a single author.
- `forget10`: Forgetting 10% of the original dataset, all entries correspond to a single author.
Retain sets corresponding to each forget set are also available, which can be used to train an Oracle model.
## Codebase
The code for training the models and availability of all fine-tuned models can be found at our [GitHub repository]().
## Citing Our Work
If you find our codebase and dataset beneficial, please cite our work:
```
@article{yourname2023tofu,
title={TOFU: Task of Fictitious Unlearning},
author={Your Name and Collaborators},
journal={Journal of Imaginary Data},
volume={1},
number={1},
pages={1--10},
year={2023},
publisher={Fictitious Publisher}
}
``` | [
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|
nreHieW/SD-Prompts | nreHieW | 2023-12-05T17:23:49Z | 84 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-04T16:09:42Z | ---
license: apache-2.0
dataset_info:
features:
- name: prompt
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1203940244.4386747
num_examples: 7331040
download_size: 559938627
dataset_size: 1203940244.4386747
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
This is a compilation of the following datasets
- FredZhang7/stable-diffusion-prompts-2.47M
- laion/dalle-3-dataset
- Multimodal-Fatima/COCO_captions_train
- recastai/LAION-art-EN-improved-captions
- succinctly/midjourney-prompts
- Geonmo/midjourney-prompts-only
- daspartho/stable-diffusion-prompts
```python
# Filtering
filtered = dataset.filter(lambda x: (len(x['prompt'].split()) > 3) and (len(x['prompt'].split()) < 150)).filter(lambda x: (sum(1 for c in x['prompt'] if c.isupper()) < 10))
```
```python
# Dedup
from itertools import cycle
from functools import partial
memory = set()
def is_unique(elem , column: str, memory: set) -> bool:
if elem[column] in memory:
return False
else:
memory.add(elem[column])
return True
dedup = processed.filter(partial(is_unique, column="prompt", memory=memory))
```
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|
chenglu/hf-blogs-baai-embeddings | chenglu | 2023-12-09T05:07:11Z | 84 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-09T05:06:44Z | ---
license: apache-2.0
---
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|
yashraizada/top-reviews-per-business | yashraizada | 2023-12-09T19:40:59Z | 81 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-09T19:36:22Z | ---
license: apache-2.0
---
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|
open-llm-leaderboard/details_SUSTech__SUS-Chat-34B | open-llm-leaderboard | 2023-12-10T10:58:42Z | 79 | 0 | [
"region:us"
]
| null | 2023-12-04T18:30:06Z | ---
pretty_name: Evaluation run of SUSTech/SUS-Chat-34B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B) 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_SUSTech__SUS-Chat-34B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-10T10:55:45.455909](https://huggingface.co/datasets/open-llm-leaderboard/details_SUSTech__SUS-Chat-34B/blob/main/results_2023-12-10T10-55-45.455909.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\": 0.7604055831741483,\n\
\ \"acc_stderr\": 0.02829305624294987,\n \"acc_norm\": 0.7636323040929514,\n\
\ \"acc_norm_stderr\": 0.028842862208073416,\n \"mc1\": 0.40636474908200737,\n\
\ \"mc1_stderr\": 0.017193835812093897,\n \"mc2\": 0.5704122295242341,\n\
\ \"mc2_stderr\": 0.014843409183922712\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6356655290102389,\n \"acc_stderr\": 0.014063260279882419,\n\
\ \"acc_norm\": 0.6629692832764505,\n \"acc_norm_stderr\": 0.013813476652902274\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6400119498107947,\n\
\ \"acc_stderr\": 0.004790155370993446,\n \"acc_norm\": 0.8390758812985462,\n\
\ \"acc_norm_stderr\": 0.003667099594023359\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.725925925925926,\n\
\ \"acc_stderr\": 0.03853254836552003,\n \"acc_norm\": 0.725925925925926,\n\
\ \"acc_norm_stderr\": 0.03853254836552003\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8881578947368421,\n \"acc_stderr\": 0.025648341251693605,\n\
\ \"acc_norm\": 0.8881578947368421,\n \"acc_norm_stderr\": 0.025648341251693605\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\
\ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \
\ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02461829819586651,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02461829819586651\n },\n\
\ \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9166666666666666,\n\
\ \"acc_stderr\": 0.023112508176051236,\n \"acc_norm\": 0.9166666666666666,\n\
\ \"acc_norm_stderr\": 0.023112508176051236\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n\
\ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7283236994219653,\n\
\ \"acc_stderr\": 0.03391750322321659,\n \"acc_norm\": 0.7283236994219653,\n\
\ \"acc_norm_stderr\": 0.03391750322321659\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.049665709039785295,\n\
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.049665709039785295\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7872340425531915,\n \"acc_stderr\": 0.026754391348039783,\n\
\ \"acc_norm\": 0.7872340425531915,\n \"acc_norm_stderr\": 0.026754391348039783\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5614035087719298,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.03600105692727771,\n\
\ \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.03600105692727771\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.7116402116402116,\n \"acc_stderr\": 0.023330654054535892,\n \"\
acc_norm\": 0.7116402116402116,\n \"acc_norm_stderr\": 0.023330654054535892\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.6031746031746031,\n\
\ \"acc_stderr\": 0.043758884927270585,\n \"acc_norm\": 0.6031746031746031,\n\
\ \"acc_norm_stderr\": 0.043758884927270585\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8935483870967742,\n\
\ \"acc_stderr\": 0.01754510295165663,\n \"acc_norm\": 0.8935483870967742,\n\
\ \"acc_norm_stderr\": 0.01754510295165663\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.6354679802955665,\n \"acc_stderr\": 0.0338640574606209,\n\
\ \"acc_norm\": 0.6354679802955665,\n \"acc_norm_stderr\": 0.0338640574606209\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\"\
: 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706467,\n\
\ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706467\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9191919191919192,\n \"acc_stderr\": 0.019417681889724536,\n \"\
acc_norm\": 0.9191919191919192,\n \"acc_norm_stderr\": 0.019417681889724536\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\
\ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8102564102564103,\n \"acc_stderr\": 0.019880165406588803,\n\
\ \"acc_norm\": 0.8102564102564103,\n \"acc_norm_stderr\": 0.019880165406588803\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.43333333333333335,\n \"acc_stderr\": 0.030213340289237927,\n \
\ \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.030213340289237927\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8739495798319328,\n \"acc_stderr\": 0.021559623121213928,\n\
\ \"acc_norm\": 0.8739495798319328,\n \"acc_norm_stderr\": 0.021559623121213928\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4966887417218543,\n \"acc_stderr\": 0.04082393379449654,\n \"\
acc_norm\": 0.4966887417218543,\n \"acc_norm_stderr\": 0.04082393379449654\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9247706422018349,\n \"acc_stderr\": 0.011308662537571729,\n \"\
acc_norm\": 0.9247706422018349,\n \"acc_norm_stderr\": 0.011308662537571729\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6574074074074074,\n \"acc_stderr\": 0.032365852526021574,\n \"\
acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.032365852526021574\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073322,\n \"\
acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073322\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.9156118143459916,\n \"acc_stderr\": 0.01809424711647332,\n \
\ \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.01809424711647332\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n\
\ \"acc_stderr\": 0.02737309550054019,\n \"acc_norm\": 0.7892376681614349,\n\
\ \"acc_norm_stderr\": 0.02737309550054019\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n\
\ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035206,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035206\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.03038159675665168,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.03038159675665168\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8895705521472392,\n \"acc_stderr\": 0.024624937788941318,\n\
\ \"acc_norm\": 0.8895705521472392,\n \"acc_norm_stderr\": 0.024624937788941318\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5714285714285714,\n\
\ \"acc_stderr\": 0.04697113923010213,\n \"acc_norm\": 0.5714285714285714,\n\
\ \"acc_norm_stderr\": 0.04697113923010213\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.9029126213592233,\n \"acc_stderr\": 0.029315962918813474,\n\
\ \"acc_norm\": 0.9029126213592233,\n \"acc_norm_stderr\": 0.029315962918813474\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\
\ \"acc_stderr\": 0.01700436856813235,\n \"acc_norm\": 0.9273504273504274,\n\
\ \"acc_norm_stderr\": 0.01700436856813235\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263714,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263714\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9054916985951469,\n\
\ \"acc_stderr\": 0.010461015338193068,\n \"acc_norm\": 0.9054916985951469,\n\
\ \"acc_norm_stderr\": 0.010461015338193068\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8208092485549133,\n \"acc_stderr\": 0.020647590029679332,\n\
\ \"acc_norm\": 0.8208092485549133,\n \"acc_norm_stderr\": 0.020647590029679332\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6826815642458101,\n\
\ \"acc_stderr\": 0.01556639263005703,\n \"acc_norm\": 0.6826815642458101,\n\
\ \"acc_norm_stderr\": 0.01556639263005703\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.02082375883758091,\n\
\ \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02082375883758091\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8167202572347267,\n\
\ \"acc_stderr\": 0.021974198848265823,\n \"acc_norm\": 0.8167202572347267,\n\
\ \"acc_norm_stderr\": 0.021974198848265823\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8611111111111112,\n \"acc_stderr\": 0.01924252622654454,\n\
\ \"acc_norm\": 0.8611111111111112,\n \"acc_norm_stderr\": 0.01924252622654454\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6418439716312057,\n \"acc_stderr\": 0.02860208586275942,\n \
\ \"acc_norm\": 0.6418439716312057,\n \"acc_norm_stderr\": 0.02860208586275942\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6160365058670143,\n\
\ \"acc_stderr\": 0.01242158783313423,\n \"acc_norm\": 0.6160365058670143,\n\
\ \"acc_norm_stderr\": 0.01242158783313423\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.023157468308559352,\n\
\ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.023157468308559352\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.8169934640522876,\n \"acc_stderr\": 0.015643069911273337,\n \
\ \"acc_norm\": 0.8169934640522876,\n \"acc_norm_stderr\": 0.015643069911273337\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.043502714429232425,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.043502714429232425\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8326530612244898,\n \"acc_stderr\": 0.023897144768914524,\n\
\ \"acc_norm\": 0.8326530612244898,\n \"acc_norm_stderr\": 0.023897144768914524\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\
\ \"acc_stderr\": 0.021628920516700643,\n \"acc_norm\": 0.8955223880597015,\n\
\ \"acc_norm_stderr\": 0.021628920516700643\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n\
\ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\
\ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8947368421052632,\n \"acc_stderr\": 0.02353755765789256,\n\
\ \"acc_norm\": 0.8947368421052632,\n \"acc_norm_stderr\": 0.02353755765789256\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40636474908200737,\n\
\ \"mc1_stderr\": 0.017193835812093897,\n \"mc2\": 0.5704122295242341,\n\
\ \"mc2_stderr\": 0.014843409183922712\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.835043409629045,\n \"acc_stderr\": 0.010430917468237424\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7217589082638363,\n \
\ \"acc_stderr\": 0.012343803671422683\n }\n}\n```"
repo_url: https://huggingface.co/SUSTech/SUS-Chat-34B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|arc:challenge|25_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|arc:challenge|25_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|gsm8k|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|gsm8k|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hellaswag|10_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hellaswag|10_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T18-27-20.173218.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T10-55-45.455909.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T10-55-45.455909.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- '**/details_harness|winogrande|5_2023-12-04T18-27-20.173218.parquet'
- split: 2023_12_10T10_55_45.455909
path:
- '**/details_harness|winogrande|5_2023-12-10T10-55-45.455909.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-10T10-55-45.455909.parquet'
- config_name: results
data_files:
- split: 2023_12_04T18_27_20.173218
path:
- results_2023-12-04T18-27-20.173218.parquet
- split: 2023_12_10T10_55_45.455909
path:
- results_2023-12-10T10-55-45.455909.parquet
- split: latest
path:
- results_2023-12-10T10-55-45.455909.parquet
---
# Dataset Card for Evaluation run of SUSTech/SUS-Chat-34B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/SUSTech/SUS-Chat-34B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B) 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_SUSTech__SUS-Chat-34B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-10T10:55:45.455909](https://huggingface.co/datasets/open-llm-leaderboard/details_SUSTech__SUS-Chat-34B/blob/main/results_2023-12-10T10-55-45.455909.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": 0.7604055831741483,
"acc_stderr": 0.02829305624294987,
"acc_norm": 0.7636323040929514,
"acc_norm_stderr": 0.028842862208073416,
"mc1": 0.40636474908200737,
"mc1_stderr": 0.017193835812093897,
"mc2": 0.5704122295242341,
"mc2_stderr": 0.014843409183922712
},
"harness|arc:challenge|25": {
"acc": 0.6356655290102389,
"acc_stderr": 0.014063260279882419,
"acc_norm": 0.6629692832764505,
"acc_norm_stderr": 0.013813476652902274
},
"harness|hellaswag|10": {
"acc": 0.6400119498107947,
"acc_stderr": 0.004790155370993446,
"acc_norm": 0.8390758812985462,
"acc_norm_stderr": 0.003667099594023359
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.725925925925926,
"acc_stderr": 0.03853254836552003,
"acc_norm": 0.725925925925926,
"acc_norm_stderr": 0.03853254836552003
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8881578947368421,
"acc_stderr": 0.025648341251693605,
"acc_norm": 0.8881578947368421,
"acc_norm_stderr": 0.025648341251693605
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8,
"acc_stderr": 0.02461829819586651,
"acc_norm": 0.8,
"acc_norm_stderr": 0.02461829819586651
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.9166666666666666,
"acc_stderr": 0.023112508176051236,
"acc_norm": 0.9166666666666666,
"acc_norm_stderr": 0.023112508176051236
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695238,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695238
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7283236994219653,
"acc_stderr": 0.03391750322321659,
"acc_norm": 0.7283236994219653,
"acc_norm_stderr": 0.03391750322321659
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5294117647058824,
"acc_stderr": 0.049665709039785295,
"acc_norm": 0.5294117647058824,
"acc_norm_stderr": 0.049665709039785295
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7872340425531915,
"acc_stderr": 0.026754391348039783,
"acc_norm": 0.7872340425531915,
"acc_norm_stderr": 0.026754391348039783
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5614035087719298,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.5614035087719298,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7517241379310344,
"acc_stderr": 0.03600105692727771,
"acc_norm": 0.7517241379310344,
"acc_norm_stderr": 0.03600105692727771
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.7116402116402116,
"acc_stderr": 0.023330654054535892,
"acc_norm": 0.7116402116402116,
"acc_norm_stderr": 0.023330654054535892
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.6031746031746031,
"acc_stderr": 0.043758884927270585,
"acc_norm": 0.6031746031746031,
"acc_norm_stderr": 0.043758884927270585
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8935483870967742,
"acc_stderr": 0.01754510295165663,
"acc_norm": 0.8935483870967742,
"acc_norm_stderr": 0.01754510295165663
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6354679802955665,
"acc_stderr": 0.0338640574606209,
"acc_norm": 0.6354679802955665,
"acc_norm_stderr": 0.0338640574606209
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8666666666666667,
"acc_stderr": 0.026544435312706467,
"acc_norm": 0.8666666666666667,
"acc_norm_stderr": 0.026544435312706467
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9191919191919192,
"acc_stderr": 0.019417681889724536,
"acc_norm": 0.9191919191919192,
"acc_norm_stderr": 0.019417681889724536
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9689119170984456,
"acc_stderr": 0.012525310625527033,
"acc_norm": 0.9689119170984456,
"acc_norm_stderr": 0.012525310625527033
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8102564102564103,
"acc_stderr": 0.019880165406588803,
"acc_norm": 0.8102564102564103,
"acc_norm_stderr": 0.019880165406588803
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.43333333333333335,
"acc_stderr": 0.030213340289237927,
"acc_norm": 0.43333333333333335,
"acc_norm_stderr": 0.030213340289237927
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8739495798319328,
"acc_stderr": 0.021559623121213928,
"acc_norm": 0.8739495798319328,
"acc_norm_stderr": 0.021559623121213928
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4966887417218543,
"acc_stderr": 0.04082393379449654,
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}
```
### 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] | [
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|
krishan-CSE/HatEval | krishan-CSE | 2023-12-05T04:26:34Z | 79 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T03:41:53Z | ---
license: apache-2.0
---
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|
pminervini/HaluEval | pminervini | 2023-12-07T14:14:47Z | 79 | 2 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-06T09:00:12Z | ---
license: apache-2.0
dataset_info:
- config_name: dialogue
features:
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dtype: string
- name: dialogue_history
dtype: string
- name: right_response
dtype: string
- name: hallucinated_response
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splits:
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num_examples: 10000
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dataset_size: 6212598
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features:
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splits:
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num_examples: 10000
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dataset_size: 5303515
- config_name: general
features:
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dtype: string
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sequence: string
splits:
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num_examples: 4507
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dataset_size: 2905898
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features:
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splits:
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dataset_size: 5486422
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features:
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splits:
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download_size: 3427901
dataset_size: 5112196
- config_name: summarization
features:
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dtype: string
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splits:
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dataset_size: 46408787
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features:
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splits:
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num_bytes: 42743472
num_examples: 10000
download_size: 25506458
dataset_size: 42743472
configs:
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data_files:
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path: dialogue/data-*
- config_name: dialogue_samples
data_files:
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path: dialogue_samples/data-*
- config_name: general
data_files:
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path: general/data-*
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data_files:
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path: qa/data-*
- config_name: qa_samples
data_files:
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path: qa_samples/data-*
- config_name: summarization
data_files:
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path: summarization/data-*
- config_name: summarization_samples
data_files:
- split: data
path: summarization_samples/data-*
---
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|
NomaDamas/DSTC-11-Track-5 | NomaDamas | 2023-12-08T12:39:44Z | 78 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-08T12:26:54Z | ---
license: apache-2.0
dataset_info:
- config_name: default
features:
- name: log
list:
- name: speaker
dtype: string
- name: text
dtype: string
- name: target
dtype: bool
- name: knowledge
list:
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dtype: int64
- name: doc_type
dtype: string
- name: domain
dtype: string
- name: entity_id
dtype: int64
- name: sent_id
dtype: int64
- name: response
dtype: string
splits:
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num_bytes: 22289817
num_examples: 28431
- name: test
num_bytes: 4412204
num_examples: 5475
- name: validation
num_bytes: 3371855
num_examples: 4173
download_size: 12543490
dataset_size: 30073876
- config_name: knowledge
features:
- name: domain
dtype: string
- name: entity_id
dtype: int64
- name: entity_name
dtype: string
- name: doc_type
dtype: string
- name: doc_id
dtype: string
- name: review_sent_id
dtype: string
- name: review_sentence
dtype: string
- name: review_metadata
struct:
- name: dishes
sequence: string
- name: drinks
sequence: string
- name: traveler_type
dtype: string
- name: faq_question
dtype: string
- name: faq_answer
dtype: string
splits:
- name: train
num_bytes: 2135411
num_examples: 10882
download_size: 535623
dataset_size: 2135411
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
- config_name: knowledge
data_files:
- split: train
path: knowledge/train-*
---
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|
nayohan/msdg-eval | nayohan | 2023-12-09T22:27:08Z | 76 | 0 | [
"region:us"
]
| null | 2023-12-09T21:32:50Z | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: test
num_bytes: 406016
num_examples: 100
download_size: 218562
dataset_size: 406016
---
# Dataset Card for "msdg-eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
duncsand/english_pii-1k | duncsand | 2023-12-06T14:01:12Z | 74 | 0 | [
"region:us"
]
| null | 2023-12-04T16:03:53Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3088707
num_examples: 10000
download_size: 1606680
dataset_size: 3088707
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
sordonia/adauni-v3-10k-flat | sordonia | 2023-12-05T06:31:11Z | 73 | 0 | [
"region:us"
]
| null | 2023-12-04T20:11:28Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: task_name
dtype: string
- name: task_source
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 5978550918.385344
num_examples: 3896711
download_size: 3899706220
dataset_size: 5978550918.385344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "adauni-v3-10k-flat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Nexusflow/Function_Call_Definitions | Nexusflow | 2023-12-05T07:08:39Z | 71 | 4 | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-12-05T06:04:42Z | ---
license: cc-by-nc-sa-4.0
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- name: function_calls
dtype: string
- name: descriptions
dtype: string
splits:
- name: train
num_bytes: 11501
num_examples: 12
download_size: 11668
dataset_size: 11501
configs:
- config_name: CVECPE
data_files:
- split: train
path: CVECPE/train-*
- config_name: CVECPE_Multi (Nested)
data_files:
- split: train
path: CVECPE_Multi (Nested)/train-*
- config_name: Climate
data_files:
- split: train
path: Climate/train-*
- config_name: OTX
data_files:
- split: train
path: OTX/train-*
- config_name: Places
data_files:
- split: train
path: Places/train-*
- config_name: VT_Multi (Nested)
data_files:
- split: train
path: VT_Multi (Nested)/train-*
- config_name: VT_Multi (Parallel)
data_files:
- split: train
path: VT_Multi (Parallel)/train-*
- config_name: VirusTotal
data_files:
- split: train
path: VirusTotal/train-*
---
# Dataset Card for "Nexusflow/Function_Call_Definitions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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|
punwaiw/diffusionJockeyDynamics | punwaiw | 2023-12-05T09:16:50Z | 70 | 0 | [
"region:us"
]
| null | 2023-12-04T22:40:41Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: dynamics
dtype: image
splits:
- name: train
num_bytes: 3946214461.25
num_examples: 17635
download_size: 3916831572
dataset_size: 3946214461.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
heloisy/cosmos_qa_ptbr | heloisy | 2023-12-08T12:53:09Z | 70 | 0 | [
"task_categories:multiple-choice",
"size_categories:10K<n<100K",
"language:pt",
"license:cc-by-4.0",
"region:us"
]
| [
"multiple-choice"
]
| 2023-12-05T16:10:33Z | ---
pretty_name: CosmosQA Português
language:
- pt
license:
- cc-by-4.0
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: contexto
dtype: string
- name: pergunta
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: Label
dtype: string
- name: id
dtype: string
splits:
- name: train
num_examples: 25262
- name: validation
num_examples: 2985
- name: test
num_examples: 6963
task_categories:
- multiple-choice
---
# Cosmos QA Português
Este dataset é uma tradução para português do Cosmos QA, que originalmente é na língua inglesa.
A tradução foi feita automaticamente usando o GPT-3.5-turbo, logo pode ter erros que não foram notados numa análise superficial.
Se atente ao uso.
# Dataset Card for cosmos_qa
## Licensing Information
The data is distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
## Source Data Citation INformation
```
@inproceedings{huang-etal-2019-cosmos,
title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
author = "Huang, Lifu and
Le Bras, Ronan and
Bhagavatula, Chandra and
Choi, Yejin",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1243",
doi = "10.18653/v1/D19-1243",
pages = "2391--2401",
}
``` | [
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|
statworx/pitchfork_album_reviews | statworx | 2023-12-06T08:30:01Z | 70 | 0 | [
"region:us"
]
| null | 2023-12-06T08:14:06Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: artist
dtype: string
- name: album
dtype: string
- name: genre
dtype: string
- name: author
dtype: string
- name: score
dtype: float64
- name: review
dtype: string
- name: augmented_review
dtype: string
splits:
- name: train
num_bytes: 158520395
num_examples: 19305
- name: test
num_bytes: 12860529
num_examples: 1566
download_size: 116445666
dataset_size: 171380924
---
# Dataset Card for "pitchfork_album_reviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
hahminlew/kream-product-blip-captions | hahminlew | 2023-12-07T11:02:10Z | 69 | 2 | [
"task_categories:text-to-image",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-sa-4.0",
"fashion",
"cloth",
"computer-vision",
"region:us"
]
| [
"text-to-image"
]
| 2023-10-10T23:39:49Z | ---
license: cc-by-nc-sa-4.0
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1363424468
num_examples: 14904
download_size: 1328309729
dataset_size: 1363424468
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-to-image
language:
- en
size_categories:
- 10K<n<100K
images_reference:
- KREAM (https://kream.co.kr/)
pretty_name: KREAM Product Blip Capitions
tags:
- fashion
- cloth
- computer-vision
---
## KREAM Product Blip Captions Dataset Information
**KREAM Product Blip Captions Dataset** is a dataset card for finetuning a text-to-image generative model collected from [KREAM](https://kream.co.kr/), one of the best online-resell market in Korea.
This dataset consists of 'image' and 'text' key pairs.
The format of 'text' is 'category (e.g. outer), product original name (e.g. The North Face 1996 Eco Nuptse Jacket Black), blip captions (e.g. a photography of the north face black down jacket)'.
You can easily construct this dataset and finetune stable diffusion from scratch using [fashion-product-generator](https://github.com/hahminlew/fashion-product-generator).
## Usage
```
from datasets import load_dataset
dataset = load_dataset("hahminlew/kream-product-blip-captions", split="train")
sample = dataset[0]
display(sample["image"].resize((256, 256)))
print(sample["text"])
```
```
outer, The North Face 1996 Eco Nuptse Jacket Black, a photography of the north face black down jacket
```
## Application
You can inference the finetuned Stable Diffusion XL with LoRA based on the dataset here: [hahminlew/sdxl-kream-model-lora-2.0](https://huggingface.co/hahminlew/sdxl-kream-model-lora-2.0)
## Citation
If you use KREAM Product Dataset in your research or projects, please cite it as:
```
@misc{lew2023kream,
author = {Lew, Hah Min},
title = {KREAM Product BLIP Captions},
year={2023},
howpublished= {\url{https://huggingface.co/datasets/hahminlew/kream-product-blip-captions/}}
}
``` | [
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|
OpenLLM-France/Claire-Dialogue-French-0.1 | OpenLLM-France | 2023-12-05T09:14:53Z | 69 | 17 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:conversational",
"task_ids:language-modeling",
"task_ids:dialogue-modeling",
"task_ids:dialogue-generation",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:fr",
"license:cc-by-nc-sa-4.0",
"conversational",
"text-generation",
"conditional-text-generation",
"dialogue-modeling",
"dialogue-generation",
"arxiv:2311.16840",
"region:us"
]
| [
"text-generation",
"text2text-generation",
"conversational"
]
| 2023-11-27T21:16:05Z | ---
pretty_name: Claire French Dialogue Dataset (CFDD)
license: cc-by-nc-sa-4.0
language:
- fr
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
task_categories:
- text-generation
- text2text-generation
- conversational
task_ids:
- language-modeling
- dialogue-modeling
- dialogue-generation
tags:
- conversational
- text-generation
- conditional-text-generation
- dialogue-modeling
- dialogue-generation
viewer: true
configs:
- config_name: default
sample_by: paragraph
data_files:
- split: train
path: "FR/*/train.txt"
- split: test
path: "FR/*/test.txt"
---
# Claire French Dialogue Dataset (CFDD) <br /> _A collection of French dialogue transcripts and plays_
This is the first packaged version of the datasets used to train the Claire family of large language models
([OpenLLM-France/Claire-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-0.1)).
The Claire French Dialogue Dataset (CFDD) is a collection of theater plays and transcripts of real French dialogues from various sources, including parliamentary proceedings, interviews, debates, meetings, and free conversations.
Each dialogue is split into speech turns, and each speech turn is labeled with the name of the speaker, or a unique identifier if the speaker is unknown.
* [Dataset composition](#dataset-composition)
* [Data sources](#data-sources)
* [Example use (python)](#example-use-python)
* [Important notes](#important-notes)
* [License](#license)
* [Citations](#citations)
* [Contact](#contact)
## Dataset composition
CFDD can be broken down into:
* 37 015 conversations in total (36 731 in train, 284 in test)
* 2 961 116 speech turns in total (2 934 084 in train, 27 032 in test)
* around 150M words
It is a collection of several independent datasets, classified by the types of conversations they contain. This categorization is designed to more evenly balance the influence of different styles of dialogue on model training and to facilitate future applications of CFDD for which certain types of dialogue might be more helpful than others.
Note that this categorization leads to multiple cases in which the original corpus is split into subcorpora. When the smaller sets are included in our corpus, they are clearly indicated, e.g., "ESLO (1/5)". Some portions of the original corpora have been excluded entirely because they did not include dialogue between adults (e.g., monologues, read literature).
For more information, you can look at the following documents:
* [FR/metadata.csv](FR/metadata.csv) contains further statistics on the different subcorpora (broken down by train/test splits).
* [FR/metadata_filter_datasets_regex.json](FR/metadata_filter_datasets_regex.json) contains information about how original datasets were filtered and/or split into sub-categories.
* [FR/metadata_split_testset_list.json](FR/metadata_split_testset_list.json) contains information about which files in the original datasets were chosen to be in the test set.
### Data sources
<table>
<thead>
<tr>
<th>Dataset</th>
<th>Sub-folder(s)</th>
<th>Description</th>
<th>Words</th>
<th>Turns</th>
<th>Conversations</th>
<th>License (and conditions)</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Parliamentary Proceedings</strong></em></u></td></tr>
<tr>
<td><a href="https://www.assemblee-nationale.fr">Assemblée Nationale</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR"><code>FR/AssembleeNationale*</code></a> <br /> <em>(one folder per legislative period)</em></td>
<td>Parliamentary proceedings from the French National Assembly</td>
<td>133M</td>
<td>1.6M</td>
<td>4.5k</td>
<td><a href="https://www.etalab.gouv.fr/wp-content/uploads/2017/04/ETALAB-Licence-Ouverte-v2.0.pdf">Open License 2.0</a></td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Theatre</strong></em></u></td></tr>
<tr>
<td><a href="https://dracor.org/fre">Theatre Classique</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TheatreClassique"><code>FR/TheatreClassique</code></a></td>
<td>Classic stage plays</td>
<td>12.8M</td>
<td>441k</td>
<td>25k</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://github.com/dracor-org/fredracor#to-cite-fredracor-">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://theatregratuit.com">Theatre Gratuit</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TheatreGratuit"><code>FR/TheatreGratuit</code></a></td>
<td>Stage plays</td>
<td>2.7M</td>
<td>155k</td>
<td>4k</td>
<td></td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Interviews</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (1/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_interview"><code>FR/ESLO_interview</code></a></td>
<td>Guided conversations</td>
<td>4.2M</td>
<td>329k</td>
<td>399</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.cnrtl.fr/corpus/tcof/">TCOF</a> (adults)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TCOF_adults"><code>FR/TCOF_adults</code></a></td>
<td>Guided conversations (between adults)</td>
<td>765k</td>
<td>49k</td>
<td>237</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/2.0/">CC BY-NC-SA 2.0</a> (<a href="https://www.ortolang.fr/market/corpora/tcof">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://cfpp2000.univ-paris3.fr/Presentation.html">CFPP</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CFPP"><code>FR/CFPP</code></a></td>
<td>Interviews of people in Paris in 2000</td>
<td>608k</td>
<td>48k</td>
<td>42</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://www.ortolang.fr/market/corpora/cfpp2000">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Valibel</a> (1/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_valibel_interview"><code>FR/ORFEO_valibel_interview</code></a></td>
<td>Guided conversations of Belgian French speakers</td>
<td>458k</td>
<td>19k</td>
<td>67</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/pfc">PFC</a> (1/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/PFC_guided"><code>FR/PFC_guided</code></a></td>
<td>Guided interviews</td>
<td>268k</td>
<td>15k</td>
<td>173</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/pfc">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://ortolang107.inist.fr/?f%5BnomCorpus%5D%5B%5D=ORFEO_cfpb+%28O%29&locale=fr">ORFEO/CFPB</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_cfpb"><code>FR/ORFEO_cfpb</code></a></td>
<td>Interviews of people in Brussels</td>
<td>138k</td>
<td>11k</td>
<td>12</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/sldr000832">ACSYNT</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ACSYNT"><code>FR/ACSYNT</code></a></td>
<td>Guided interviews from southwestern France</td>
<td>61k</td>
<td>2.7k</td>
<td>144</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/sldr000832">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Free Conversations</strong></em></u></td></tr>
<tr>
<td><a href="https://ofrom.unine.ch/">OFROM</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/OFROM"><code>FR/OFROM</code></a></td>
<td>Conversations in French-speaking Switzerland</td>
<td>590k</td>
<td>44k</td>
<td>151</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://ofrom.unine.ch/index.php?page=citations">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (2/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_free"><code>FR/ESLO_free</code></a></td>
<td>Diverse conversation</td>
<td>480k</td>
<td>47k</td>
<td>98</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/CRFP</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_crfp"><code>FR/ORFEO_crfp</code></a></td>
<td>Diverse conversations</td>
<td>405k</td>
<td>9k</td>
<td>124</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/C-ORAL-ROM</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_coralrom"><code>FR/ORFEO_coralrom</code></a></td>
<td>Diverse conversation</td>
<td>248k</td>
<td>6k</td>
<td>152</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/pfc">PFC</a> (2/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/PFC_free"><code>FR/PFC_free</code></a></td>
<td>Diverse conversation</td>
<td>230k</td>
<td>14k</td>
<td>146</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/pfc">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://clapi.ish-lyon.cnrs.fr/">CLAPI</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CLAPI"><code>FR/CLAPI</code></a></td>
<td>Diverse conversation</td>
<td>122k</td>
<td>15k</td>
<td>14</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/sldr000720">CID</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CID"><code>FR/CID</code></a></td>
<td>Dialogues between two friends</td>
<td>118k</td>
<td>9k</td>
<td>8</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/sldr000720">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://rhapsodie.modyco.fr/">Rhapsodie</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/Rhapsodie"><code>FR/Rhapsodie</code></a></td>
<td>Diverse conversations</td>
<td>28k</td>
<td>1k</td>
<td>41</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://rhapsodie.modyco.fr/propriete-intellectuelle/">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://github.com/surfacesyntacticud/SUD_French-ParisStories">Paris Stories</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ParisStories"><code>FR/ParisStories</code></a></td>
<td>Diverse conversations in Paris</td>
<td>28k</td>
<td>351</td>
<td>54</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a></td>
</tr>
<tr>
<td>LinTO (1/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_free"><code>FR/LINAGORA_free</code></a></td>
<td>Diverse conversation</td>
<td>26k</td>
<td>2k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Meetings</strong></em></u></td></tr>
<tr>
<td>SUMM-RE</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/SUMM-RE"><code>FR/SUMM-RE</code></a></td>
<td>Meeting-style conversations (transcribed with Whisper large-v2 ASR)</td>
<td>1.3M</td>
<td>39k</td>
<td>283</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (please cite)</td>
</tr>
<tr>
<td><a href="http://ortolang107.inist.fr/?f%5BnomCorpus%5D%5B%5D=R%C3%A9unions+de+travail+%28O%29&fnomCorpus=Chambers-Rostand+E&locale=fr">ORFEO/Reunions-de-Travail</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_reunions-de-travail"><code>FR/ORFEO_reunions-de-travail</code></a></td>
<td>Real meetings</td>
<td>210k</td>
<td>12k</td>
<td>29</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td>LinTO (2/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_meetings"><code>FR/LINAGORA_meetings</code></a></td>
<td>Meetings on speech recognition</td>
<td>41k</td>
<td>1.8k</td>
<td>6</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Debates</strong></em></u></td></tr>
<tr>
<td><a href="https://github.com/linto-ai/FREDSum">FREDSum</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/FREDSum"><code>FR/FREDSum</code></a></td>
<td>French political debates</td>
<td>406k</td>
<td>7k</td>
<td>144</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (please cite)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (3/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_conf"><code>FR/ESLO_conf</code></a></td>
<td>Conferences</td>
<td>76k</td>
<td>2k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Assistance</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (4/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_assistance"><code>FR/ESLO_assistance</code></a></td>
<td>In-person assistance and call-centers</td>
<td>95k</td>
<td>11k</td>
<td>143</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Fleuron</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_fleuron"><code>FR/ORFEO_fleuron</code></a></td>
<td>Interactions created to teach foreign students about university life</td>
<td>33k</td>
<td>2k</td>
<td>51</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.info.univ-tours.fr/~antoine/parole_publique/OTG/index.html">OTG</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/OTG"><code>FR/OTG</code></a></td>
<td>Dialogues in a tourism office</td>
<td>27k</td>
<td>4k</td>
<td>315</td>
<td><a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> (<a href="https://www.info.univ-tours.fr/~antoine/parole_publique/OTG/index.html">contact before usage</a>)</td>
</tr>
<tr>
<td><a href="https://www.info.univ-tours.fr/~antoine/parole_publique/Accueil_UBS/index.html">Accueil UBS</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/UBS"><code>FR/UBS</code></a></td>
<td>University telephone answering service</td>
<td>7.2k</td>
<td>1k</td>
<td>41</td>
<td><a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> (<a href="https://www.info.univ-tours.fr/~antoine/parole_publique/Accueil_UBS/index.html">contact before usage</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Presentation, Formal Address</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (5/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_discourse"><code>FR/ESLO_discourse</code></a></td>
<td>Conference presentations</td>
<td>43k</td>
<td>120</td>
<td>9</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td>LinTO (3/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_discourse"><code>FR/LINAGORA_discourse</code></a></td>
<td>Technical presentations (AI topics) with Q/A</td>
<td>38k</td>
<td>1.5k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Valibel</a> (2/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_valibel_discourse"><code>FR/ORFEO_valibel_discourse</code></a></td>
<td>Formal university addresses</td>
<td>12k</td>
<td>5</td>
<td>5</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
</tbody>
</table>
## Example use (python)
In the following `sample_by="paragraph"` is important to ensure that each sample corresponds to a full conversation (not just a speech turn).
Load dataset from HuggingFace cache (downloaded under `~/.cache/huggingface/datasets`):
```python
from datasets import load_dataset
dataset = load_dataset("OpenLLM-France/Claire-Dialogue-French-0.1", sample_by="paragraph", streaming=True)
```
Load dataset from raw text files:
```python
from datasets import load_dataset
import glob
path = "path/to/dataset"
train_files = glob.glob(path + "/*/train.txt")
test_files = glob.glob(path + "/*/test.txt")
dataset = load_dataset("text", data_files={"train": train_files, "test": test_files}, sample_by="paragraph", streaming=True)
```
Iterate on the dataset:
```python
for sample in dataset["train"]:
train_conversation = sample["text"]
...
for sample in dataset["test"]:
test_conversation = sample["text"]
...
```
## Important notes
All datasets were normalized in text files so that:
* Conversations are separated by a single blank line.
* Each line corresponds to a single speech turn.
* Each line begins with a speaker label of the form "`[***:]`".
* When speaker names are anonymized or otherwise unknown, speakers are distinguished by numbers in the following format: "**`[speaker001:]`**", "**`[speaker002:]`**", … <br /> Otherwise, speakers are labeled with their names or roles, e.g. "`[Paul:]`", "`[François Mitterrand:]`", "`[M. le président:]`".
* There are no parentheses: special annotations are always between square brackets.
* Commong tags include:
* "**`[PII]`**": Personally Identifiable Information (anonymized name...)
* "`[NOISE]`": distinct ambient noises
* "`[LAUGHTER]`": laughter
<!-- * Truncated words are sometimes marked with "-" (ex: "je suis dé- décidé") -->
* Depending on the data source, data may or may not include punctuation marks and upper case letters.
* The data were normalized in various ways including unicode NFC normalization, conversion of unbreakable spaces to spaces, and standardization of punctuation marks (`…` -> `...`, `«`/`»`/`“`/`”`/`″`/`„` -> `"`). <!-- `’`/`‘`/`‛`/`ʿ` -> `'`, `ᵉ`/`ᵉʳ` -> `e`/`er`, `‚` -> `,` -->
Those details are described in the paper:
[_« The Claire French Dialogue Dataset »_](https://arxiv.org/abs/2311.16840) (2023).
## License
Given that some of the corpora used for training are only available under CC-BY-NC-SA licenses,
Claire-Dialogue-French-0.1 is made available under the [CC-BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
## Citations
When using the CFDD corpus, please cite the following paper:
✍ Julie Hunter, Jérôme Louradour, Virgile Rennard, Ismaïl Harrando, Guokan Shang, Jean-Pierre Lorré (2023)
[The Claire French Dialogue Dataset](https://arxiv.org/abs/2311.16840)
```bibtex
@misc{openllm2023claire,
title={The Claire French Dialogue Dataset},
author={Julie Hunter and Jérôme Louradour and Virgile Rennard and Ismaïl Harrando and Guokan Shang and Jean-Pierre Lorré},
year={2023},
eprint={2311.16840},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
This paper in turn provides the requested citations for all of the original corpora.
The same references are also listed below.
* **Accueil UBS**
* Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). [Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_LREC_CORP.pdf). _Third European Conference on Language Resources and Evaluation_ (LREC). Las Palmas de Gran Canaria, Espagne.
* Jean-Yves Antoine, Jerome Goulian, Jeanne Villaneau, Marc le Tallec (2009). [Word Order Phenomena in Spoken French : a Study on Four Corpora of Task-Oriented Dialogue and its Consequences on Language Processing](https://www.info.univ-tours.fr/~antoine/articles/2009_Corpus_Linguistics.pdf). _Corpus Linguistics_, Liverpool, UK.
* **ACSYNT**
* Cognition, Langue, Langages, Ergonomie - UMR 5263 (CLLE) (2013). [ACSYNT [Corpus]](https://hdl.handle.net/11403/sldr000832/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage).
* **CFPP**
* Branca-Rosoff S., Fleury S., Lefeuvre F., Pires M., 2012, [Discours sur la ville. Présentation du Corpus de Français Parlé Parisien des années 2000](http://cfpp2000.univ-paris3.fr/CFPP2000.pdf) (CFPP2000).
* CLESTHIA - Langage, systèmes, discours - EA 7345 (CLESTHIA) (2018). [CFPP2000 [Corpus]](https://hdl.handle.net/11403/cfpp2000/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **CID**
* Roxane Bertrand, Philippe Blache, Robert Espesser, Gaëlle Ferré, Christine Meunier, Béatrice Priego-Valverde, Stéphane Rauzy (2008). [Le CID — Corpus of Interactional Data — Annotation et Exploitation Multimodale de Parole Conversationnelle](https://hal.science/hal-00349893). _Traitement Automatique des Langues_, vol. 49, no. 3.
* Philippe Blache, Roxane Bertrand, Brigitte Bigi et al. (2010). [Multimodal annotation of conversational data](http://portal.acm.org/citation.cfm?id=1868749). _Proceedings of the Fourth Linguistic Annotation Workshop_.
* Laboratoire parole et langage - UMR 7309 (LPL) (2021). [Transcriptions du corpus CID [Corpus]](https://hdl.handle.net/11403/sldr000720/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage).
* **CLAPI**
* CLAPI, [http://clapi.icar.cnrs.fr](http://clapi.icar.cnrs.fr)
* Groupe ICOR (H. Baldauf-Quilliatre, I. Colon de Carvajal, C. Etienne, E. Jouin-Chardon, S. Teston-Bonnard, V. Traverso) (2016). [CLAPI, une base de données multimodale pour la parole en interaction : apports et dilemmes](https://shs.hal.science/halshs-01316283/). In Avanzi M., Béguelin M.-J. & Diémoz F. (eds), _Corpus de français parlés et français parlés des corpus, Corpus_ 15.
* **ESLO**
* Iris Eshkol-Taravella, Olivier Baude, Denis Maurel, Linda Hriba, Céline Dugua, Isabelle Tellier (2012). Un grand corpus oral « disponible » : le corpus d’Orléans 1968-2012, _Ressources linguistiques libres, TAL_. Volume 52 – n° 3/2011, 17-46.
* Laboratoire Ligérien de Linguistique - UMR 7270 (LLL) (2023). [ESLO [Corpus]](https://hdl.handle.net/11403/eslo/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **FREDSum**
* Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis (forthcoming). FREDSum: A Dialogue Summarization Corpus for French Political Debates. _Findings of Empirical Methods in Natural Language Processsing_ (EMNLP).
* **LinTO**
* Lila Gravellier, Julie Hunter, Philippe Muller, Thomas Pellegrini, Isabelle Ferrané (2021).
[Weakly Supervised Discourse Segmentation for Multiparty Oral Conversation](https://aclanthology.org/2021.emnlp-main.104/).
_The 2021 Conference on Empirical Methods in Natural Language Processing_ (EMNLP), pp. 1381–1392.
* **OFROM**
* Mathieu Avanzi, Marie-José Béguelin, Gilles Corminboeuf, Federica Diémoz, Laure Anne Johnsen (2012-2023). [Corpus OFROM – Corpus oral de français de Suisse romande](ofrom.unine.ch). Université de Neuchâtel.
* Mathieu Avanzi, Marie-José Béguelin, Federica Diémoz (2016). [Présentation du corpus OFROM – Corpus oral de français de Suisse romande](ofrom.unine.ch/uploads/Documents/AM-MJB-FD_GC_LAJ_OFROM_23.pdf). Université de Neuchâtel.
* Mathieu Avanzi, Marie-José Béguelin, Federica Diémoz (2016). De l’archive de parole au corpus de référence. Le corpus oral de français de Suisse romande (OFROM). _Actes du colloque Corpus de Français Parlés et Français Parlés des Corpus_ (= Corpus 15), 309-342.
* Gilles Corminboeuf, Julie Rothenbühler, Maguelone Sauzet (éds) (2020). [Français parlés et français ‘tout court’](studialinguisticaromanica.org/index.php/slr/issue/view/4). _Studia Linguistica Romanica_ n°4, publication électronique.
* **ORFEO** (pour chaque corpus issu du projet ORFEO)
* Jeanne-Marie Debaisieux, Christophe Benzitoun, Henri-José Deulofeu. Le projet ORFÉO : un corpus d’étude pour le français contemporain, _Corpus 15_, Actes du colloque Corpus de Français Parlés et Français Parlés des Corpus.
* Carruthers, Janice (2008). Annotating an Oral Corpus using the Text Encoding Initiative. Methodology, Problems, Solutions, _Journal of French Language Studies_ 18(1), 103-119.
* Agnès Tutin, Francis Grossmann (2014). _L’écrit scientifique : du lexique au discours. Autour de Scientext_. Presses de l’Université de Rennes.
* **ORFEO/CFPB**
* Anne Dister, Emmanuelle Labeau (2017). [Le corpus de français parlé à Bruxelles: origines, hypothèses, développements et prédictions](https://www.semanticscholar.org/paper/Le-corpus-de-fran%C3%A7ais-parl%C3%A9-%C3%A0-Bruxelles%3A-origines%2C-Dister-Labeau/0fe858f6b8c1ce49a2e43b34494c2e76922162fa).
* **ORFEO/C-Oral-Rom**
* Cresti Emanuela, Bacelar do Nascimento Fernanda, Moreno Sandoval Antonio, Veronis Jean, Martin Philippe, Kalid Choukri (2005). The C-ORAL-ROM CORPUS: A Multilingual Resource of Spontaneous Speech for Romance Languages. _Studies in Corpus Linguistics_, 15. John Benjamins Publishing Company 304 pp. (incl. DVD).
* **ORFEO/CRFP**
* Équipe Delic (2004). Recherches sur le français parlé n° 18, « Autour du Corpus de référence du français parlé » Publications de l’université de Provence, 265 p.
* **ORFEO/Valibel**
* Anne Dister, Michel Francard, Philippe Hambye, Anne-Catherine Simon (2009). [Du corpus à la banque de données. Du son, des textes et des métadonnées. L'évolution de banque de données textuelles orales VALIBEL (1989-2009)](https://cdn.uclouvain.be/public/Exports%20reddot/valibel/documents/Dister_et_al_2009_Cahiers.pdf), _Cahiers de Linguistique_ 33/2, 113-129.
* **OTG**
* Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). [Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_LREC_CORP.pdf). _Third European Conference on Language Resources and Evaluation_ (LREC). Las Palmas de Gran Canaria, Espagne.
* Jean-Yves Antoine, Sabine Letellier-Zarshenas, Pascale Nicolas, Igor Schadle (2002). [Corpus OTG et ECOLE_MASSY : vers la constitution d’un collection de corpus francophones de dialogue oral diffusés librement](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_TALN_CORP.pdf). _Actes TALN_ 2002. Nancy, France.
* **Paris Stories**
* Sylvain Kahane, Bernard Caron, Emmett Strickland, Kim Gerdes. Annotation guidelines of UD and SUD treebanks for spoken corpora: A proposal. _Proceedings of the 20th International Workshop on Treebanks and Linguistic Theories_ (TLT, SyntaxFest 2021).
* **PFC**
* Jacques Durand, Bernard Laks, Chantal Lyche (2009). Le projet PFC: une source de données primaires structurées. In J. Durand, B. Laks et C. Lyche (eds)(2009) _Phonologie, variation et accents du français_. Paris: Hermès. pp. 19-61.
* Modèles, Dynamiques, Corpus - UMR 7114 (MoDyCo), Université de Groningen (RUG) (2017). [PFC - Phonologie du Français Contemporain [Corpus]](https://hdl.handle.net/11403/pfc/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **Rhapsodie**
* see [https://rhapsodie.modyco.fr/propriete-intellectuelle/](https://rhapsodie.modyco.fr/propriete-intellectuelle/)
* **SUMM-RE**
* Hiroyoshi Yamasaki, Jérôme Louradour, Julie Hunter, Laurent Prévot (forthcoming). Transcribing And Aligning Conversational Speech: A Hybrid Pipeline Applied To French Conversations. _Workshop on Automatic Speech Recognition and Understanding_ (ASRU).
* **TCOF**
* Analyse et Traitement Informatique de la Langue Française (2020). [TCOF : Traitement de Corpus Oraux en Français [Corpus]](https://www.ortolang.fr/market/corpora/tcof/v2.1). _ORTOLANG (Open Resources and TOols for LANGuage)_
* **Theatre Classique**
* French Drama Corpus (FreDraCor): A TEI P5 Version of Paul Fièvre's "Théâtre Classique" Corpus. Edited by Carsten Milling, Frank Fischer and Mathias Göbel. Hosted on GitHub, 2021 – [https://github.com/dracor-org/fredracor](https://github.com/dracor-org/fredracor)
## Contact
[email protected] | [
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|
BoooomNing/CNetImg2Img-samples | BoooomNing | 2023-12-05T14:00:25Z | 68 | 0 | [
"region:us"
]
| null | 2023-11-27T18:07:09Z | ---
dataset_info:
features:
- name: id
dtype: int32
- name: input_image
dtype: image
- name: edit_pose
dtype: image
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 821850038.924
num_examples: 4754
download_size: 817189072
dataset_size: 821850038.924
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
cmu-mlsp/librispeech960-wavlm-large-km1000_asr_tokenized_final | cmu-mlsp | 2023-12-05T14:33:35Z | 68 | 0 | [
"region:us"
]
| null | 2023-12-05T06:28:08Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: validation_tts
path: data/validation_tts-*
- split: test
path: data/test-*
- split: test_tts
path: data/test_tts-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 4809631893
num_examples: 281241
- name: validation
num_bytes: 54319982
num_examples: 5406
- name: validation_tts
num_bytes: 27159991
num_examples: 2703
- name: test
num_bytes: 27180211
num_examples: 2620
- name: test_tts
num_bytes: 27180211
num_examples: 2620
download_size: 506035712
dataset_size: 4945472288
---
# Dataset Card for "librispeech960-wavlm-large-km1000_asr_tokenized_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
laion/meta-imagine-dataset | laion | 2023-12-08T03:14:11Z | 68 | 0 | [
"region:us"
]
| null | 2023-12-08T02:37:03Z | ---
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 0
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
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