datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
AmrutaMuthal/controlnet_layout2image_scaled_filled_boxes_wt_masks_unsharded | ---
dataset_info:
features:
- name: image
dtype: image
- name: 'Unnamed: 0.1'
dtype: int64
- name: 'Unnamed: 0'
dtype: int64
- name: caption
dtype: string
- name: conditioning_image
dtype: image
- name: mask_image
dtype: image
- name: obj_bbox_mask
dtype: image
splits:
- name: train
num_bytes: 21958045746.036
num_examples: 19996
download_size: 1953960387
dataset_size: 21958045746.036
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gaygaaa/THEMOVIEDATASET | ---
license: mit
---
|
open-llm-leaderboard/details_liminerity__Mistral-quiet-star | ---
pretty_name: Evaluation run of liminerity/Mistral-quiet-star
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [liminerity/Mistral-quiet-star](https://huggingface.co/liminerity/Mistral-quiet-star)\
\ 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_liminerity__Mistral-quiet-star\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-24T15:07:57.118558](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Mistral-quiet-star/blob/main/results_2024-03-24T15-07-57.118558.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\": 0.6178730622844697,\n\
\ \"acc_stderr\": 0.032593405931812494,\n \"acc_norm\": 0.6240141761556957,\n\
\ \"acc_norm_stderr\": 0.033257630813890666,\n \"mc1\": 0.30354957160342716,\n\
\ \"mc1_stderr\": 0.016095884155386854,\n \"mc2\": 0.450998665908648,\n\
\ \"mc2_stderr\": 0.015659336336238144\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5767918088737202,\n \"acc_stderr\": 0.014438036220848029,\n\
\ \"acc_norm\": 0.6117747440273038,\n \"acc_norm_stderr\": 0.014241614207414044\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6610237004580761,\n\
\ \"acc_stderr\": 0.0047239435490059765,\n \"acc_norm\": 0.845947022505477,\n\
\ \"acc_norm_stderr\": 0.0036026174466413925\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n\
\ \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800897,\n\
\ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800897\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\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\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146267,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146267\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137285,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137285\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\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\": 0.7225806451612903,\n\
\ \"acc_stderr\": 0.025470196835900055,\n \"acc_norm\": 0.7225806451612903,\n\
\ \"acc_norm_stderr\": 0.025470196835900055\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\
: 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\
\ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\
\ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.024396672985094757,\n\
\ \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.024396672985094757\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.031204691225150016,\n\
\ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.031204691225150016\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8,\n \"acc_stderr\": 0.01714985851425095,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.01714985851425095\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n\
\ \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\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\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069432,\n \
\ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069432\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\
\ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\
\ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.03512385283705049,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.03512385283705049\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7969348659003831,\n\
\ \"acc_stderr\": 0.014385525076611578,\n \"acc_norm\": 0.7969348659003831,\n\
\ \"acc_norm_stderr\": 0.014385525076611578\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.024685316867257803,\n\
\ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.024685316867257803\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.26033519553072626,\n\
\ \"acc_stderr\": 0.014676252009319473,\n \"acc_norm\": 0.26033519553072626,\n\
\ \"acc_norm_stderr\": 0.014676252009319473\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\
\ \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.6881028938906752,\n\
\ \"acc_norm_stderr\": 0.02631185807185416\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.025630824975621348,\n\
\ \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.025630824975621348\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \
\ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44654498044328556,\n\
\ \"acc_stderr\": 0.012697046024399678,\n \"acc_norm\": 0.44654498044328556,\n\
\ \"acc_norm_stderr\": 0.012697046024399678\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6323529411764706,\n \"acc_stderr\": 0.02928941340940319,\n\
\ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.02928941340940319\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6437908496732027,\n \"acc_stderr\": 0.0193733324207245,\n \
\ \"acc_norm\": 0.6437908496732027,\n \"acc_norm_stderr\": 0.0193733324207245\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\
\ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.028782108105401712,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.028782108105401712\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30354957160342716,\n\
\ \"mc1_stderr\": 0.016095884155386854,\n \"mc2\": 0.450998665908648,\n\
\ \"mc2_stderr\": 0.015659336336238144\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.771112865035517,\n \"acc_stderr\": 0.011807360224025395\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.32221379833206976,\n \
\ \"acc_stderr\": 0.012872435481188778\n }\n}\n```"
repo_url: https://huggingface.co/liminerity/Mistral-quiet-star
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|arc:challenge|25_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|gsm8k|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hellaswag|10_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-24T15-07-57.118558.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-24T15-07-57.118558.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- '**/details_harness|winogrande|5_2024-03-24T15-07-57.118558.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-24T15-07-57.118558.parquet'
- config_name: results
data_files:
- split: 2024_03_24T15_07_57.118558
path:
- results_2024-03-24T15-07-57.118558.parquet
- split: latest
path:
- results_2024-03-24T15-07-57.118558.parquet
---
# Dataset Card for Evaluation run of liminerity/Mistral-quiet-star
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [liminerity/Mistral-quiet-star](https://huggingface.co/liminerity/Mistral-quiet-star) 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_liminerity__Mistral-quiet-star",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-24T15:07:57.118558](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Mistral-quiet-star/blob/main/results_2024-03-24T15-07-57.118558.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": 0.6178730622844697,
"acc_stderr": 0.032593405931812494,
"acc_norm": 0.6240141761556957,
"acc_norm_stderr": 0.033257630813890666,
"mc1": 0.30354957160342716,
"mc1_stderr": 0.016095884155386854,
"mc2": 0.450998665908648,
"mc2_stderr": 0.015659336336238144
},
"harness|arc:challenge|25": {
"acc": 0.5767918088737202,
"acc_stderr": 0.014438036220848029,
"acc_norm": 0.6117747440273038,
"acc_norm_stderr": 0.014241614207414044
},
"harness|hellaswag|10": {
"acc": 0.6610237004580761,
"acc_stderr": 0.0047239435490059765,
"acc_norm": 0.845947022505477,
"acc_norm_stderr": 0.0036026174466413925
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.631578947368421,
"acc_stderr": 0.03925523381052932,
"acc_norm": 0.631578947368421,
"acc_norm_stderr": 0.03925523381052932
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.028637235639800897,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.028637235639800897
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.044084400227680794,
"acc_norm": 0.74,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146267,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146267
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.025107425481137285,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.025107425481137285
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.043758884927270605,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.043758884927270605
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7225806451612903,
"acc_stderr": 0.025470196835900055,
"acc_norm": 0.7225806451612903,
"acc_norm_stderr": 0.025470196835900055
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7393939393939394,
"acc_stderr": 0.034277431758165236,
"acc_norm": 0.7393939393939394,
"acc_norm_stderr": 0.034277431758165236
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7676767676767676,
"acc_stderr": 0.030088629490217487,
"acc_norm": 0.7676767676767676,
"acc_norm_stderr": 0.030088629490217487
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8652849740932642,
"acc_stderr": 0.024639789097709443,
"acc_norm": 0.8652849740932642,
"acc_norm_stderr": 0.024639789097709443
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6358974358974359,
"acc_stderr": 0.024396672985094757,
"acc_norm": 0.6358974358974359,
"acc_norm_stderr": 0.024396672985094757
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.028493465091028597,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028597
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6386554621848739,
"acc_stderr": 0.031204691225150016,
"acc_norm": 0.6386554621848739,
"acc_norm_stderr": 0.031204691225150016
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
"acc_stderr": 0.038796870240733264,
"acc_norm": 0.3443708609271523,
"acc_norm_stderr": 0.038796870240733264
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8,
"acc_stderr": 0.01714985851425095,
"acc_norm": 0.8,
"acc_norm_stderr": 0.01714985851425095
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"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": 0.7721518987341772,
"acc_stderr": 0.027303484599069432,
"acc_norm": 0.7721518987341772,
"acc_norm_stderr": 0.027303484599069432
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6547085201793722,
"acc_stderr": 0.03191100192835794,
"acc_norm": 0.6547085201793722,
"acc_norm_stderr": 0.03191100192835794
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7633587786259542,
"acc_stderr": 0.03727673575596914,
"acc_norm": 0.7633587786259542,
"acc_norm_stderr": 0.03727673575596914
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7239263803680982,
"acc_stderr": 0.03512385283705049,
"acc_norm": 0.7239263803680982,
"acc_norm_stderr": 0.03512385283705049
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507333,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7969348659003831,
"acc_stderr": 0.014385525076611578,
"acc_norm": 0.7969348659003831,
"acc_norm_stderr": 0.014385525076611578
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.024685316867257803,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.024685316867257803
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.26033519553072626,
"acc_stderr": 0.014676252009319473,
"acc_norm": 0.26033519553072626,
"acc_norm_stderr": 0.014676252009319473
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7189542483660131,
"acc_stderr": 0.025738854797818733,
"acc_norm": 0.7189542483660131,
"acc_norm_stderr": 0.025738854797818733
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6881028938906752,
"acc_stderr": 0.02631185807185416,
"acc_norm": 0.6881028938906752,
"acc_norm_stderr": 0.02631185807185416
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6944444444444444,
"acc_stderr": 0.025630824975621348,
"acc_norm": 0.6944444444444444,
"acc_norm_stderr": 0.025630824975621348
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.029752389657427047,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.029752389657427047
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.44654498044328556,
"acc_stderr": 0.012697046024399678,
"acc_norm": 0.44654498044328556,
"acc_norm_stderr": 0.012697046024399678
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6323529411764706,
"acc_stderr": 0.02928941340940319,
"acc_norm": 0.6323529411764706,
"acc_norm_stderr": 0.02928941340940319
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6437908496732027,
"acc_stderr": 0.0193733324207245,
"acc_norm": 0.6437908496732027,
"acc_norm_stderr": 0.0193733324207245
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7020408163265306,
"acc_stderr": 0.029279567411065677,
"acc_norm": 0.7020408163265306,
"acc_norm_stderr": 0.029279567411065677
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8606965174129353,
"acc_stderr": 0.024484487162913973,
"acc_norm": 0.8606965174129353,
"acc_norm_stderr": 0.024484487162913973
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.028782108105401712,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.028782108105401712
},
"harness|truthfulqa:mc|0": {
"mc1": 0.30354957160342716,
"mc1_stderr": 0.016095884155386854,
"mc2": 0.450998665908648,
"mc2_stderr": 0.015659336336238144
},
"harness|winogrande|5": {
"acc": 0.771112865035517,
"acc_stderr": 0.011807360224025395
},
"harness|gsm8k|5": {
"acc": 0.32221379833206976,
"acc_stderr": 0.012872435481188778
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
JacenQ/nd_ae_android_dataset | ---
license: apache-2.0
---
|
Starlee822/dataset1 | ---
license: openrail
---
|
pencaharlangit/hand-gesture-small-3000 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 24676892.235
num_examples: 2991
download_size: 23795975
dataset_size: 24676892.235
---
# Dataset Card for "hand-gesture-small-3000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anan-2024/twitter_dataset_1713135887 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 29317
num_examples: 73
download_size: 15272
dataset_size: 29317
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AravindVadlapudi02/UA_speech_noisereduced_10c10p | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': healthy control
'1': pathology
- name: input_features
sequence:
sequence: float32
splits:
- name: train
num_bytes: 3830764348
num_examples: 3989
- name: test
num_bytes: 1536531200
num_examples: 1600
download_size: 620634914
dataset_size: 5367295548
---
# Dataset Card for "UA_speech_noisereduced_10c10p"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zolak/twitter_dataset_78_1713203416 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 4010732
num_examples: 10065
download_size: 2031905
dataset_size: 4010732
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_rte_subord_conjunction_doubling | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 13700
num_examples: 29
- name: train
num_bytes: 11779
num_examples: 28
download_size: 26861
dataset_size: 25479
---
# Dataset Card for "MULTI_VALUE_rte_subord_conjunction_doubling"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lshowway/wikipedia.reorder.osv.pl | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1958124685
num_examples: 1772445
download_size: 548655232
dataset_size: 1958124685
---
# Dataset Card for "wikipedia.reorder.osv.pl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_chatty123__mistral_rank16_packing | ---
pretty_name: Evaluation run of chatty123/mistral_rank16_packing
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chatty123/mistral_rank16_packing](https://huggingface.co/chatty123/mistral_rank16_packing)\
\ 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_chatty123__mistral_rank16_packing\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-15T17:49:21.120438](https://huggingface.co/datasets/open-llm-leaderboard/details_chatty123__mistral_rank16_packing/blob/main/results_2024-04-15T17-49-21.120438.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\": 0.6032682186013162,\n\
\ \"acc_stderr\": 0.03330769446425311,\n \"acc_norm\": 0.6080811662540284,\n\
\ \"acc_norm_stderr\": 0.03398423334560759,\n \"mc1\": 0.5201958384332925,\n\
\ \"mc1_stderr\": 0.017489216849737057,\n \"mc2\": 0.6744371383175135,\n\
\ \"mc2_stderr\": 0.015254727441468672\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520763,\n\
\ \"acc_norm\": 0.6254266211604096,\n \"acc_norm_stderr\": 0.014144193471893454\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6584345747858992,\n\
\ \"acc_stderr\": 0.004732654295724447,\n \"acc_norm\": 0.8478390758812986,\n\
\ \"acc_norm_stderr\": 0.00358442749057938\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\
\ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\
\ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.039531733777491945,\n\
\ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.039531733777491945\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\
\ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n\
\ \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n\
\ \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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_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\": 0.5780346820809249,\n\
\ \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n\
\ \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\
\ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5191489361702127,\n \"acc_stderr\": 0.03266204299064678,\n\
\ \"acc_norm\": 0.5191489361702127,\n \"acc_norm_stderr\": 0.03266204299064678\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\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.38095238095238093,\n \"acc_stderr\": 0.025010749116137605,\n \"\
acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.025010749116137605\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\
\ \"acc_stderr\": 0.04343525428949098,\n \"acc_norm\": 0.38095238095238093,\n\
\ \"acc_norm_stderr\": 0.04343525428949098\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.6774193548387096,\n \"acc_stderr\": 0.026593084516572277,\n \"\
acc_norm\": 0.6774193548387096,\n \"acc_norm_stderr\": 0.026593084516572277\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"\
acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.63,\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\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\
\ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365897,\n \"\
acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365897\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153314,\n\
\ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153314\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5538461538461539,\n \"acc_stderr\": 0.02520357177302833,\n \
\ \"acc_norm\": 0.5538461538461539,\n \"acc_norm_stderr\": 0.02520357177302833\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3333333333333333,\n \"acc_stderr\": 0.02874204090394848,\n \
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.02874204090394848\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.03120469122515002,\n \
\ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.03120469122515002\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154343,\n \"\
acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154343\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.44907407407407407,\n \"acc_stderr\": 0.03392238405321616,\n \"\
acc_norm\": 0.44907407407407407,\n \"acc_norm_stderr\": 0.03392238405321616\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7549019607843137,\n \"acc_stderr\": 0.03019028245350195,\n \"\
acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.03019028245350195\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \
\ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6278026905829597,\n\
\ \"acc_stderr\": 0.032443052830087304,\n \"acc_norm\": 0.6278026905829597,\n\
\ \"acc_norm_stderr\": 0.032443052830087304\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.040393149787245605,\n\
\ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.040393149787245605\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7037037037037037,\n\
\ \"acc_stderr\": 0.04414343666854933,\n \"acc_norm\": 0.7037037037037037,\n\
\ \"acc_norm_stderr\": 0.04414343666854933\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.03487825168497892,\n\
\ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.03487825168497892\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.022801382534597552,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.022801382534597552\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.01486682166470958,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.01486682166470958\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.02541600377316554,\n\
\ \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.02541600377316554\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3452513966480447,\n\
\ \"acc_stderr\": 0.015901432608930365,\n \"acc_norm\": 0.3452513966480447,\n\
\ \"acc_norm_stderr\": 0.015901432608930365\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.026643278474508755,\n\
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.026643278474508755\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6752411575562701,\n\
\ \"acc_stderr\": 0.026596782287697043,\n \"acc_norm\": 0.6752411575562701,\n\
\ \"acc_norm_stderr\": 0.026596782287697043\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6759259259259259,\n \"acc_stderr\": 0.02604176620271716,\n\
\ \"acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.02604176620271716\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4574468085106383,\n \"acc_stderr\": 0.029719281272236844,\n \
\ \"acc_norm\": 0.4574468085106383,\n \"acc_norm_stderr\": 0.029719281272236844\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43089960886571055,\n\
\ \"acc_stderr\": 0.012647695889547235,\n \"acc_norm\": 0.43089960886571055,\n\
\ \"acc_norm_stderr\": 0.012647695889547235\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5992647058823529,\n \"acc_stderr\": 0.029768263528933105,\n\
\ \"acc_norm\": 0.5992647058823529,\n \"acc_norm_stderr\": 0.029768263528933105\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.619281045751634,\n \"acc_stderr\": 0.019643801557924803,\n \
\ \"acc_norm\": 0.619281045751634,\n \"acc_norm_stderr\": 0.019643801557924803\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\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7562189054726368,\n\
\ \"acc_stderr\": 0.03036049015401464,\n \"acc_norm\": 0.7562189054726368,\n\
\ \"acc_norm_stderr\": 0.03036049015401464\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366255,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366255\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835816,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835816\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5201958384332925,\n\
\ \"mc1_stderr\": 0.017489216849737057,\n \"mc2\": 0.6744371383175135,\n\
\ \"mc2_stderr\": 0.015254727441468672\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.771112865035517,\n \"acc_stderr\": 0.011807360224025391\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3912054586808188,\n \
\ \"acc_stderr\": 0.013442502402794302\n }\n}\n```"
repo_url: https://huggingface.co/chatty123/mistral_rank16_packing
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|arc:challenge|25_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|gsm8k|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hellaswag|10_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T17-49-21.120438.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T17-49-21.120438.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- '**/details_harness|winogrande|5_2024-04-15T17-49-21.120438.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-15T17-49-21.120438.parquet'
- config_name: results
data_files:
- split: 2024_04_15T17_49_21.120438
path:
- results_2024-04-15T17-49-21.120438.parquet
- split: latest
path:
- results_2024-04-15T17-49-21.120438.parquet
---
# Dataset Card for Evaluation run of chatty123/mistral_rank16_packing
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [chatty123/mistral_rank16_packing](https://huggingface.co/chatty123/mistral_rank16_packing) 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_chatty123__mistral_rank16_packing",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-15T17:49:21.120438](https://huggingface.co/datasets/open-llm-leaderboard/details_chatty123__mistral_rank16_packing/blob/main/results_2024-04-15T17-49-21.120438.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": 0.6032682186013162,
"acc_stderr": 0.03330769446425311,
"acc_norm": 0.6080811662540284,
"acc_norm_stderr": 0.03398423334560759,
"mc1": 0.5201958384332925,
"mc1_stderr": 0.017489216849737057,
"mc2": 0.6744371383175135,
"mc2_stderr": 0.015254727441468672
},
"harness|arc:challenge|25": {
"acc": 0.5750853242320819,
"acc_stderr": 0.014445698968520763,
"acc_norm": 0.6254266211604096,
"acc_norm_stderr": 0.014144193471893454
},
"harness|hellaswag|10": {
"acc": 0.6584345747858992,
"acc_stderr": 0.004732654295724447,
"acc_norm": 0.8478390758812986,
"acc_norm_stderr": 0.00358442749057938
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.618421052631579,
"acc_stderr": 0.039531733777491945,
"acc_norm": 0.618421052631579,
"acc_norm_stderr": 0.039531733777491945
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"acc_stderr": 0.02881561571343211,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.02881561571343211
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6597222222222222,
"acc_stderr": 0.039621355734862175,
"acc_norm": 0.6597222222222222,
"acc_norm_stderr": 0.039621355734862175
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956913,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956913
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5780346820809249,
"acc_stderr": 0.0376574669386515,
"acc_norm": 0.5780346820809249,
"acc_norm_stderr": 0.0376574669386515
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5191489361702127,
"acc_stderr": 0.03266204299064678,
"acc_norm": 0.5191489361702127,
"acc_norm_stderr": 0.03266204299064678
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.046446020912223177,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.046446020912223177
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.025010749116137605,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.025010749116137605
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.04343525428949098,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.04343525428949098
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6774193548387096,
"acc_stderr": 0.026593084516572277,
"acc_norm": 0.6774193548387096,
"acc_norm_stderr": 0.026593084516572277
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7212121212121212,
"acc_stderr": 0.03501438706296781,
"acc_norm": 0.7212121212121212,
"acc_norm_stderr": 0.03501438706296781
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
"acc_stderr": 0.030954055470365897,
"acc_norm": 0.7474747474747475,
"acc_norm_stderr": 0.030954055470365897
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.844559585492228,
"acc_stderr": 0.026148483469153314,
"acc_norm": 0.844559585492228,
"acc_norm_stderr": 0.026148483469153314
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5538461538461539,
"acc_stderr": 0.02520357177302833,
"acc_norm": 0.5538461538461539,
"acc_norm_stderr": 0.02520357177302833
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.02874204090394848,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.02874204090394848
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6386554621848739,
"acc_stderr": 0.03120469122515002,
"acc_norm": 0.6386554621848739,
"acc_norm_stderr": 0.03120469122515002
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.0386155754625517,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.0386155754625517
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8036697247706422,
"acc_stderr": 0.017030719339154343,
"acc_norm": 0.8036697247706422,
"acc_norm_stderr": 0.017030719339154343
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.44907407407407407,
"acc_stderr": 0.03392238405321616,
"acc_norm": 0.44907407407407407,
"acc_norm_stderr": 0.03392238405321616
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7549019607843137,
"acc_stderr": 0.03019028245350195,
"acc_norm": 0.7549019607843137,
"acc_norm_stderr": 0.03019028245350195
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7510548523206751,
"acc_stderr": 0.028146970599422644,
"acc_norm": 0.7510548523206751,
"acc_norm_stderr": 0.028146970599422644
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6278026905829597,
"acc_stderr": 0.032443052830087304,
"acc_norm": 0.6278026905829597,
"acc_norm_stderr": 0.032443052830087304
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6946564885496184,
"acc_stderr": 0.040393149787245605,
"acc_norm": 0.6946564885496184,
"acc_norm_stderr": 0.040393149787245605
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.04414343666854933,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.04414343666854933
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7300613496932515,
"acc_stderr": 0.03487825168497892,
"acc_norm": 0.7300613496932515,
"acc_norm_stderr": 0.03487825168497892
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7475728155339806,
"acc_stderr": 0.04301250399690878,
"acc_norm": 0.7475728155339806,
"acc_norm_stderr": 0.04301250399690878
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8589743589743589,
"acc_stderr": 0.022801382534597552,
"acc_norm": 0.8589743589743589,
"acc_norm_stderr": 0.022801382534597552
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.01486682166470958,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.01486682166470958
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.02541600377316554,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.02541600377316554
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3452513966480447,
"acc_stderr": 0.015901432608930365,
"acc_norm": 0.3452513966480447,
"acc_norm_stderr": 0.015901432608930365
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6830065359477124,
"acc_stderr": 0.026643278474508755,
"acc_norm": 0.6830065359477124,
"acc_norm_stderr": 0.026643278474508755
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6752411575562701,
"acc_stderr": 0.026596782287697043,
"acc_norm": 0.6752411575562701,
"acc_norm_stderr": 0.026596782287697043
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6759259259259259,
"acc_stderr": 0.02604176620271716,
"acc_norm": 0.6759259259259259,
"acc_norm_stderr": 0.02604176620271716
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4574468085106383,
"acc_stderr": 0.029719281272236844,
"acc_norm": 0.4574468085106383,
"acc_norm_stderr": 0.029719281272236844
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.43089960886571055,
"acc_stderr": 0.012647695889547235,
"acc_norm": 0.43089960886571055,
"acc_norm_stderr": 0.012647695889547235
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5992647058823529,
"acc_stderr": 0.029768263528933105,
"acc_norm": 0.5992647058823529,
"acc_norm_stderr": 0.029768263528933105
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.619281045751634,
"acc_stderr": 0.019643801557924803,
"acc_norm": 0.619281045751634,
"acc_norm_stderr": 0.019643801557924803
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7090909090909091,
"acc_stderr": 0.04350271442923243,
"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.04350271442923243
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.0282638899437846,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.0282638899437846
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7562189054726368,
"acc_stderr": 0.03036049015401464,
"acc_norm": 0.7562189054726368,
"acc_norm_stderr": 0.03036049015401464
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366255,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366255
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5120481927710844,
"acc_stderr": 0.03891364495835816,
"acc_norm": 0.5120481927710844,
"acc_norm_stderr": 0.03891364495835816
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5201958384332925,
"mc1_stderr": 0.017489216849737057,
"mc2": 0.6744371383175135,
"mc2_stderr": 0.015254727441468672
},
"harness|winogrande|5": {
"acc": 0.771112865035517,
"acc_stderr": 0.011807360224025391
},
"harness|gsm8k|5": {
"acc": 0.3912054586808188,
"acc_stderr": 0.013442502402794302
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
gonul/turkishReviews-ds-mini | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 1251308.7426744807
num_examples: 3378
- name: validation
num_bytes: 139281.25732551946
num_examples: 376
download_size: 0
dataset_size: 1390590.0
---
# Dataset Card for "turkishReviews-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
OpenDevin/SWE-bench-devin-passed | ---
license: mit
dataset_info:
features:
- name: repo
dtype: string
- name: instance_id
dtype: string
- name: base_commit
dtype: string
- name: patch
dtype: string
- name: test_patch
dtype: string
- name: problem_statement
dtype: string
- name: hints_text
dtype: string
- name: created_at
dtype: string
- name: version
dtype: string
- name: FAIL_TO_PASS
dtype: string
- name: PASS_TO_PASS
dtype: string
- name: environment_setup_commit
dtype: string
splits:
- name: test
num_bytes: 1442151.0265911072
num_examples: 79
download_size: 299539
dataset_size: 1442151.0265911072
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
jarod1212/radiotherapy_assistant | ---
license: mit
---
|
anonymouse03052002/kishoretrial | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 261708.972
num_examples: 439
- name: validation
num_bytes: 29211.252
num_examples: 49
download_size: 132338
dataset_size: 290920.224
---
# Dataset Card for "kishoretrial"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HaloJimmy/Crossfit | ---
license: unknown
---
|
alighasemi/fa-paraphrase | ---
Tasks:
- Text2Text Generation
Fine-Grained Tasks:
- paraphrase
- query-paraphrasing
Languages:
- Persian
Multilinguality:
- monolingual
- fa
- fa-IR
Sizes:
- n>1M
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 139373682.4
num_examples: 881408
- name: test
num_bytes: 17421710.3
num_examples: 110176
- name: validation
num_bytes: 17421710.3
num_examples: 110176
download_size: 98032993
dataset_size: 174217103.00000003
---
# Dataset Card for "fa-paraphrase"
This dataset contains over 1.1 million rows. Each row contains a pair of Farsi sentences which are a paraphrase of each other. The datasets used to create this dataset can be found here:
* [tapaco](https://huggingface.co/datasets/tapaco)
* [kaggle](https://www.kaggle.com/datasets/armannikkhah/persian-paraphrase-dataset)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kaleemWaheed/twitter_dataset_1713042335 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 34355
num_examples: 88
download_size: 18475
dataset_size: 34355
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tyzhu/find_second_sent_train_10_eval_10_hint10 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 40008
num_examples: 30
- name: validation
num_bytes: 9749
num_examples: 10
download_size: 45762
dataset_size: 49757
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "find_second_sent_train_10_eval_10_hint10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Seanxh/twitter_dataset_1713201781 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 115388
num_examples: 270
download_size: 44665
dataset_size: 115388
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Patricio18/tableTotext | ---
license: unknown
---
|
qgyd2021/few_shot_translation_sft | ---
license: apache-2.0
task_categories:
- question-answering
- translation
- conversational
- text-generation
- text2text-generation
language:
- zh
- en
size_categories:
- 100M<n<1B
---
## 句子翻译指令数据集
其中包含**机器翻译**数据集，也包含**汉语文言文与白话文之间的翻译**数据集。
在做[qgyd2021/few_shot_intent_sft](https://huggingface.co/datasets/qgyd2021/few_shot_intent_sft)时，我意识到可能需要同时让模型具有翻译的能力以实现知识在不同语言之间的传递，因此决定制作此数据集。
|
LightFury9/dys_train | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text_label
dtype: string
splits:
- name: train
num_bytes: 480013795.2
num_examples: 5600
download_size: 429982174
dataset_size: 480013795.2
---
# Dataset Card for "dys_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/shun_bluearchive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shun/春原シュン/瞬 (Blue Archive)
This is the dataset of shun/春原シュン/瞬 (Blue Archive), containing 500 images and their tags.
The core tags of this character are `black_hair, long_hair, animal_ears, green_eyes, tiger_ears, halo, animal_ear_fluff, twintails, breasts, tiger_girl, extra_ears`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 707.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shun_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 602.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shun_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1330 | 1.26 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shun_bluearchive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/shun_bluearchive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, black_dress, black_footwear, china_dress, shoes, short_sleeves, solo, white_thighhighs, looking_at_viewer, smile, blunt_bangs, open_mouth, simple_background, white_background, blush, full_body, weapon_case |
| 1 | 11 |  |  |  |  |  | 1girl, black_dress, black_footwear, china_dress, looking_at_viewer, short_sleeves, smile, solo, white_background, white_thighhighs, full_body, mary_janes, simple_background, blush, closed_mouth, standing, holding |
| 2 | 17 |  |  |  |  |  | 1girl, black_dress, china_dress, looking_at_viewer, short_sleeves, solo, blush, simple_background, white_background, white_thighhighs, smile, blunt_bangs, closed_mouth, sitting, thighs |
| 3 | 16 |  |  |  |  |  | 1girl, bare_shoulders, black_dress, cleavage, hair_ornament, looking_at_viewer, smile, large_breasts, ponytail, solo, blush, bridal_gauntlets, china_dress, closed_mouth, simple_background, very_long_hair, white_background, feather_boa, multicolored_hair, tassel |
| 4 | 13 |  |  |  |  |  | 1girl, blush, hetero, 1boy, solo_focus, vaginal, cowgirl_position, girl_on_top, open_mouth, penis, large_breasts, looking_at_viewer, nipples, black_dress, china_dress, nude, ponytail, clothed_sex, cum_in_pussy, mosaic_censoring, pov, smile, spread_legs, breasts_out, navel, sweat |
| 5 | 8 |  |  |  |  |  | 1girl, blush, completely_nude, loli, looking_at_viewer, navel, nipples, open_mouth, solo, uncensored, cleft_of_venus, collarbone, flat_chest, sweat, blue_halo, :d, barefoot, bed_sheet, blunt_bangs, lying, pussy_juice, small_breasts, stomach |
| 6 | 7 |  |  |  |  |  | 1boy, blush, hetero, loli, 1girl, black_dress, erection, solo_focus, tongue_out, china_dress, licking_penis, from_side, mosaic_censoring, blue_halo, clothed_female_nude_male, open_mouth, short_sleeves |
| 7 | 11 |  |  |  |  |  | loli, looking_at_viewer, 1girl, blush, navel, micro_bikini, simple_background, smile, solo, collarbone, small_breasts, white_background, closed_mouth, black_bikini, flat_chest, side-tie_bikini_bottom, stomach, groin, white_thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_dress | black_footwear | china_dress | shoes | short_sleeves | solo | white_thighhighs | looking_at_viewer | smile | blunt_bangs | open_mouth | simple_background | white_background | blush | full_body | weapon_case | mary_janes | closed_mouth | standing | holding | sitting | thighs | bare_shoulders | cleavage | hair_ornament | large_breasts | ponytail | bridal_gauntlets | very_long_hair | feather_boa | multicolored_hair | tassel | hetero | 1boy | solo_focus | vaginal | cowgirl_position | girl_on_top | penis | nipples | nude | clothed_sex | cum_in_pussy | mosaic_censoring | pov | spread_legs | breasts_out | navel | sweat | completely_nude | loli | uncensored | cleft_of_venus | collarbone | flat_chest | blue_halo | :d | barefoot | bed_sheet | lying | pussy_juice | small_breasts | stomach | erection | tongue_out | licking_penis | from_side | clothed_female_nude_male | micro_bikini | black_bikini | side-tie_bikini_bottom | groin |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-----------------|:--------------|:--------|:----------------|:-------|:-------------------|:--------------------|:--------|:--------------|:-------------|:--------------------|:-------------------|:--------|:------------|:--------------|:-------------|:---------------|:-----------|:----------|:----------|:---------|:-----------------|:-----------|:----------------|:----------------|:-----------|:-------------------|:-----------------|:--------------|:--------------------|:---------|:---------|:-------|:-------------|:----------|:-------------------|:--------------|:--------|:----------|:-------|:--------------|:---------------|:-------------------|:------|:--------------|:--------------|:--------|:--------|:------------------|:-------|:-------------|:-----------------|:-------------|:-------------|:------------|:-----|:-----------|:------------|:--------|:--------------|:----------------|:----------|:-----------|:-------------|:----------------|:------------|:---------------------------|:---------------|:---------------|:-------------------------|:--------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | X | X | | X | X | X | X | X | | | X | X | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 17 |  |  |  |  |  | X | X | | X | | X | X | X | X | X | X | | X | X | X | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | X | X | | X | | | X | | X | X | | | X | X | X | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 13 |  |  |  |  |  | X | X | | X | | | | | X | X | | X | | | X | | | | | | | | | | | | X | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | | | | | X | | X | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | | X | | X | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | X | | | | | | | X | | | | | X | | | | | | | | X | X | X | X | X | | | | |
| 7 | 11 |  |  |  |  |  | X | | | | | | X | X | X | X | | | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | X | X | | | | | | | X | X | | | | | | X | X | X | X |
|
mtc/cnn_dm_paraphrase_small | ---
dataset_info:
features:
- name: label
dtype: string
- name: noise
dtype: bool
- name: backtranslation
dtype: bool
- name: extraction_span
sequence: int64
- name: claim
dtype: string
- name: augmentation
dtype: float64
- name: augmentation_span
dtype: float64
- name: id
dtype: string
- name: filepath
dtype: string
- name: original_span
dtype: string
- name: paraphrase
dtype: string
splits:
- name: train
num_bytes: 15338
num_examples: 50
download_size: 16711
dataset_size: 15338
---
# Dataset Card for "cnn_dm_paraphrase"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
spdenisov/processed_word | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
- name: length
dtype: int64
splits:
- name: train
num_bytes: 118733211.26505354
num_examples: 48517
download_size: 21466771
dataset_size: 118733211.26505354
---
# Dataset Card for "processed_word"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/hikawa_hina_bangdream | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hikawa_hina/氷川日菜 (BanG Dream!)
This is the dataset of hikawa_hina/氷川日菜 (BanG Dream!), containing 500 images and their tags.
The core tags of this character are `aqua_hair, green_eyes, short_hair, bow, bangs, braid, hair_bow, side_braids`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 731.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikawa_hina_bangdream/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 426.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikawa_hina_bangdream/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1178 | 879.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikawa_hina_bangdream/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 650.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikawa_hina_bangdream/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1178 | 1.24 GiB | [Download](https://huggingface.co/datasets/CyberHarem/hikawa_hina_bangdream/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hikawa_hina_bangdream',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, solo, looking_at_viewer, twin_braids, frills, hair_ribbon, open_mouth, blush, blue_choker, white_ribbon, :d, blue_bow, collarbone, bare_shoulders, electric_guitar, white_background, blue_dress, teeth, wrist_bow, yellow_bow |
| 1 | 6 |  |  |  |  |  | 1girl, blue_ribbon, looking_at_viewer, short_sleeves, solo, alternate_hairstyle, beret, blue_bow, blue_headwear, open_mouth, pom_pom_(clothes), smile, x_hair_ornament, blue_choker, blue_dress, double-breasted, neck_ribbon, striped_bow, wrist_cuffs, back_bow, blush, earrings, frilled_sleeves, hair_ribbon, hat_flower |
| 2 | 18 |  |  |  |  |  | earrings, 1girl, solo, beret, blue_bow, blue_headwear, frilled_shirt_collar, hair_ornament, hat_bow, alternate_hairstyle, long_sleeves, looking_at_viewer, star_(symbol), brooch, striped_bow, open_mouth, :d, constellation_print, long_hair, striped_ribbon, twin_braids, capelet, upper_body, ascot, blush, bowtie, neck_ribbon, star_(sky), starry_sky_print |
| 3 | 15 |  |  |  |  |  | grey_jacket, school_uniform, 1girl, blazer, collared_shirt, long_sleeves, looking_at_viewer, solo, white_shirt, blush, twin_braids, open_mouth, yellow_bow, :d, brown_necktie, diagonal-striped_necktie, plaid_skirt, pleated_skirt, cowboy_shot, diagonal_stripes, hand_up, miniskirt, standing, upper_body, upper_teeth_only, white_background |
| 4 | 5 |  |  |  |  |  | 1girl, blush, collared_shirt, looking_at_viewer, plaid_skirt, pleated_skirt, school_uniform, simple_background, solo, twin_braids, white_shirt, black_socks, blue_necktie, blue_skirt, full_body, kneehighs, miniskirt, short_sleeves, sweater_vest, white_background, diagonal-striped_necktie, medium_hair, open_mouth, yellow_bow, breasts, grin, no_shoes, parted_lips, shadow, wariza |
| 5 | 7 |  |  |  |  |  | 2girls, sisters, twincest, yuri, long_hair, upper_body, blush, long_sleeves, looking_at_another, parted_lips |
| 6 | 7 |  |  |  |  |  | 1girl, earrings, white_gloves, looking_at_viewer, smile, solo, blush, fur-trimmed_capelet, hair_ornament, long_sleeves, red_ribbon, hat_flower, long_hair, pom_pom_(clothes), red_bow, braided_bangs, corset, dress, frills, fur-trimmed_sleeves, gift, holding_lantern, night, open_mouth, red_choker, shorts, sitting, thighhighs |
| 7 | 15 |  |  |  |  |  | 1girl, demon_horns, smile, solo, mini_crown, blush, fur_collar, heart_earrings, looking_at_viewer, clothing_cutout, cross-laced_clothes, demon_tail, striped, demon_wings, red_dress, red_gloves, bracelet, fur_trim, hairband, halloween_costume, thighhighs, black_ribbon, hair_ribbon, navel, open_mouth, pink_gloves, jack-o'-lantern, medium_breasts, polearm, polka_dot_bow |
| 8 | 7 |  |  |  |  |  | blush, detached_collar, fake_animal_ears, looking_at_viewer, medium_breasts, rabbit_ears, black_leotard, cleavage, playboy_bunny, strapless_leotard, wrist_cuffs, 1girl, bare_shoulders, cowboy_shot, long_hair, red_bowtie, standing, fishnet_pantyhose, one_eye_closed, open_mouth, swept_bangs, 2girls, covered_navel, hairband, sisters, smile, solo_focus, two-tone_background |
| 9 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, censored, open_mouth, sweat, collarbone, girl_on_top, looking_at_viewer, navel, nipples, penis, clothed_female_nude_male, clothed_sex, cowgirl_position, cum, green_hair, indoors, large_breasts, shirt, swept_bangs, tearing_up, twin_braids, vaginal, yellow_bow |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | twin_braids | frills | hair_ribbon | open_mouth | blush | blue_choker | white_ribbon | :d | blue_bow | collarbone | bare_shoulders | electric_guitar | white_background | blue_dress | teeth | wrist_bow | yellow_bow | blue_ribbon | short_sleeves | alternate_hairstyle | beret | blue_headwear | pom_pom_(clothes) | smile | x_hair_ornament | double-breasted | neck_ribbon | striped_bow | wrist_cuffs | back_bow | earrings | frilled_sleeves | hat_flower | frilled_shirt_collar | hair_ornament | hat_bow | long_sleeves | star_(symbol) | brooch | constellation_print | long_hair | striped_ribbon | capelet | upper_body | ascot | bowtie | star_(sky) | starry_sky_print | grey_jacket | school_uniform | blazer | collared_shirt | white_shirt | brown_necktie | diagonal-striped_necktie | plaid_skirt | pleated_skirt | cowboy_shot | diagonal_stripes | hand_up | miniskirt | standing | upper_teeth_only | simple_background | black_socks | blue_necktie | blue_skirt | full_body | kneehighs | sweater_vest | medium_hair | breasts | grin | no_shoes | parted_lips | shadow | wariza | 2girls | sisters | twincest | yuri | looking_at_another | white_gloves | fur-trimmed_capelet | red_ribbon | red_bow | braided_bangs | corset | dress | fur-trimmed_sleeves | gift | holding_lantern | night | red_choker | shorts | sitting | thighhighs | demon_horns | mini_crown | fur_collar | heart_earrings | clothing_cutout | cross-laced_clothes | demon_tail | striped | demon_wings | red_dress | red_gloves | bracelet | fur_trim | hairband | halloween_costume | black_ribbon | navel | pink_gloves | jack-o'-lantern | medium_breasts | polearm | polka_dot_bow | detached_collar | fake_animal_ears | rabbit_ears | black_leotard | cleavage | playboy_bunny | strapless_leotard | red_bowtie | fishnet_pantyhose | one_eye_closed | swept_bangs | covered_navel | solo_focus | two-tone_background | 1boy | hetero | censored | sweat | girl_on_top | nipples | penis | clothed_female_nude_male | clothed_sex | cowgirl_position | cum | green_hair | indoors | large_breasts | shirt | tearing_up | vaginal |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------|:---------|:--------------|:-------------|:--------|:--------------|:---------------|:-----|:-----------|:-------------|:-----------------|:------------------|:-------------------|:-------------|:--------|:------------|:-------------|:--------------|:----------------|:----------------------|:--------|:----------------|:--------------------|:--------|:------------------|:------------------|:--------------|:--------------|:--------------|:-----------|:-----------|:------------------|:-------------|:-----------------------|:----------------|:----------|:---------------|:----------------|:---------|:----------------------|:------------|:-----------------|:----------|:-------------|:--------|:---------|:-------------|:-------------------|:--------------|:-----------------|:---------|:-----------------|:--------------|:----------------|:---------------------------|:--------------|:----------------|:--------------|:-------------------|:----------|:------------|:-----------|:-------------------|:--------------------|:--------------|:---------------|:-------------|:------------|:------------|:---------------|:--------------|:----------|:-------|:-----------|:--------------|:---------|:---------|:---------|:----------|:-----------|:-------|:---------------------|:---------------|:----------------------|:-------------|:----------|:----------------|:---------|:--------|:----------------------|:-------|:------------------|:--------|:-------------|:---------|:----------|:-------------|:--------------|:-------------|:-------------|:-----------------|:------------------|:----------------------|:-------------|:----------|:--------------|:------------|:-------------|:-----------|:-----------|:-----------|:--------------------|:---------------|:--------|:--------------|:------------------|:-----------------|:----------|:----------------|:------------------|:-------------------|:--------------|:----------------|:-----------|:----------------|:--------------------|:-------------|:--------------------|:-----------------|:--------------|:----------------|:-------------|:----------------------|:-------|:---------|:-----------|:--------|:--------------|:----------|:--------|:---------------------------|:--------------|:-------------------|:------|:-------------|:----------|:----------------|:--------|:-------------|:----------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | | X | X | X | X | | | X | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 18 |  |  |  |  |  | X | X | X | X | | | X | X | | | X | X | | | | | | | | | | | X | X | X | | | | | X | X | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 15 |  |  |  |  |  | X | X | X | X | | | X | X | | | X | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | X | X | | | X | X | | | | | | | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | | X | X | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | X | | X | | X | X | | | | | | | | | | | | | | | | | | X | X | | | | | | | X | | X | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 15 |  |  |  |  |  | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | | X | | | | X | X | | | | | | X | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 9 | 6 |  |  |  |  |  | X | | X | X | | | X | X | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/serval_starrail | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of serval/セーバル/希露瓦/서벌 (Honkai: Star Rail)
This is the dataset of serval/セーバル/希露瓦/서벌 (Honkai: Star Rail), containing 55 images and their tags.
The core tags of this character are `long_hair, blue_eyes, multicolored_hair, blonde_hair, breasts, bangs, earrings, streaked_hair, blue_hair, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 55 | 127.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serval_starrail/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 55 | 52.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serval_starrail/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 139 | 118.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serval_starrail/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 55 | 101.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serval_starrail/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 139 | 201.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serval_starrail/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/serval_starrail',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, solo, bare_shoulders, jewelry, smile, black_choker, detached_sleeves, looking_at_viewer, shirt, holding, long_sleeves, crop_top, pantyhose, fingerless_gloves, upper_body |
| 1 | 5 |  |  |  |  |  | blush, completely_nude, nipples, 1girl, looking_at_viewer, navel, smile, sweat, collarbone, mosaic_censoring, pussy, 2girls, armpits, black_nails, cum, nail_polish, on_back, on_bed, parted_lips, pillow, solo_focus, thighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | bare_shoulders | jewelry | smile | black_choker | detached_sleeves | looking_at_viewer | shirt | holding | long_sleeves | crop_top | pantyhose | fingerless_gloves | upper_body | blush | completely_nude | nipples | navel | sweat | collarbone | mosaic_censoring | pussy | 2girls | armpits | black_nails | cum | nail_polish | on_back | on_bed | parted_lips | pillow | solo_focus | thighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------------|:----------|:--------|:---------------|:-------------------|:--------------------|:--------|:----------|:---------------|:-----------|:------------|:--------------------|:-------------|:--------|:------------------|:----------|:--------|:--------|:-------------|:-------------------|:--------|:---------|:----------|:--------------|:------|:--------------|:----------|:---------|:--------------|:---------|:-------------|:---------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | | | X | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
kamekazenaminato/myvocal1 | ---
license: openrail
---
|
anan-2024/twitter_dataset_1713116303 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 119668
num_examples: 320
download_size: 64493
dataset_size: 119668
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TahaCakir/enhanced_turkishReviews-generativeAI | ---
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 124354659.05627702
num_examples: 380617
- name: validation
num_bytes: 13817256.943722984
num_examples: 42291
download_size: 93684397
dataset_size: 138171916.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
loubnabnl/llama-10k-annotations | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: completion
dtype: string
- name: eval_prompt_header
dtype: string
- name: generation_config
struct:
- name: do_sample
dtype: bool
- name: temperature
dtype: float64
- name: top_p
dtype: float64
- name: metadata
struct:
- name: timestamp
dtype: string
- name: prompt
dtype: string
- name: review_model
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 51557354.2433
num_examples: 9983
download_size: 14251796
dataset_size: 51557354.2433
---
# Dataset Card for "llama-10k-annotations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HydraLM/airoboros-gpt4-1.4_list_dict | ---
dataset_info:
features:
- name: conversations
list:
- name: input
dtype: string
- name: response
dtype: string
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 57382192
num_examples: 34203
download_size: 0
dataset_size: 57382192
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "airoboros-gpt4-1.4_list_dict"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rntc/big-bigbio-ner | ---
dataset_info:
features:
- name: answer
dtype: string
- name: id
dtype: string
- name: instruction
dtype: string
- name: ner_tags
sequence: string
- name: text
dtype: string
- name: tokens
sequence: string
- name: types
sequence: string
splits:
- name: train
num_bytes: 796468363
num_examples: 169113
download_size: 156028850
dataset_size: 796468363
---
# Dataset Card for "big-bigbio-ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FINNUMBER/FINCH_TRAIN_SA_FPB_ALL_NEW_Rationale | ---
dataset_info:
features:
- name: task
dtype: string
- name: sub_task
dtype: string
- name: question
dtype: string
- name: context
dtype: float64
- name: answer
dtype: string
- name: rationale
dtype: string
- name: correct
dtype: bool
- name: instruction
dtype: string
- name: check
dtype: bool
- name: output
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 6264790
num_examples: 4681
download_size: 2515806
dataset_size: 6264790
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
qangaroo | ---
language:
- en
paperswithcode_id: null
pretty_name: qangaroo
dataset_info:
- config_name: medhop
features:
- name: query
dtype: string
- name: supports
sequence: string
- name: candidates
sequence: string
- name: answer
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 93947725
num_examples: 1620
- name: validation
num_bytes: 16463555
num_examples: 342
download_size: 339843061
dataset_size: 110411280
- config_name: masked_medhop
features:
- name: query
dtype: string
- name: supports
sequence: string
- name: candidates
sequence: string
- name: answer
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 95823986
num_examples: 1620
- name: validation
num_bytes: 16802484
num_examples: 342
download_size: 339843061
dataset_size: 112626470
- config_name: wikihop
features:
- name: query
dtype: string
- name: supports
sequence: string
- name: candidates
sequence: string
- name: answer
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 325994029
num_examples: 43738
- name: validation
num_bytes: 40869634
num_examples: 5129
download_size: 339843061
dataset_size: 366863663
- config_name: masked_wikihop
features:
- name: query
dtype: string
- name: supports
sequence: string
- name: candidates
sequence: string
- name: answer
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 348290479
num_examples: 43738
- name: validation
num_bytes: 43689810
num_examples: 5129
download_size: 339843061
dataset_size: 391980289
---
# Dataset Card for "qangaroo"
## 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:** [http://qangaroo.cs.ucl.ac.uk/index.html](http://qangaroo.cs.ucl.ac.uk/index.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.36 GB
- **Size of the generated dataset:** 981.89 MB
- **Total amount of disk used:** 2.34 GB
### Dataset Summary
We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.
Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.
Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.
The two QAngaroo datasets provide a training and evaluation resource for such methods.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### masked_medhop
- **Size of downloaded dataset files:** 339.84 MB
- **Size of the generated dataset:** 112.63 MB
- **Total amount of disk used:** 452.47 MB
An example of 'validation' looks as follows.
```
```
#### masked_wikihop
- **Size of downloaded dataset files:** 339.84 MB
- **Size of the generated dataset:** 391.98 MB
- **Total amount of disk used:** 731.82 MB
An example of 'validation' looks as follows.
```
```
#### medhop
- **Size of downloaded dataset files:** 339.84 MB
- **Size of the generated dataset:** 110.42 MB
- **Total amount of disk used:** 450.26 MB
An example of 'validation' looks as follows.
```
```
#### wikihop
- **Size of downloaded dataset files:** 339.84 MB
- **Size of the generated dataset:** 366.87 MB
- **Total amount of disk used:** 706.71 MB
An example of 'validation' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### masked_medhop
- `query`: a `string` feature.
- `supports`: a `list` of `string` features.
- `candidates`: a `list` of `string` features.
- `answer`: a `string` feature.
- `id`: a `string` feature.
#### masked_wikihop
- `query`: a `string` feature.
- `supports`: a `list` of `string` features.
- `candidates`: a `list` of `string` features.
- `answer`: a `string` feature.
- `id`: a `string` feature.
#### medhop
- `query`: a `string` feature.
- `supports`: a `list` of `string` features.
- `candidates`: a `list` of `string` features.
- `answer`: a `string` feature.
- `id`: a `string` feature.
#### wikihop
- `query`: a `string` feature.
- `supports`: a `list` of `string` features.
- `candidates`: a `list` of `string` features.
- `answer`: a `string` feature.
- `id`: a `string` feature.
### Data Splits
| name |train|validation|
|--------------|----:|---------:|
|masked_medhop | 1620| 342|
|masked_wikihop|43738| 5129|
|medhop | 1620| 342|
|wikihop |43738| 5129|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
NathanRoll/TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl | ---
dataset_info:
features:
- name: __index_level_0__
dtype: 'null'
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 582
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
venetis/VMMRdb_make_model_train | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': acura_cl
'1': acura_integra
'2': acura_legend
'3': acura_mdx
'4': acura_rdx
'5': acura_rl
'6': acura_rsx
'7': acura_tl
'8': acura_tsx
'9': audi_a3
'10': audi_a4
'11': audi_a6
'12': audi_a8
'13': audi_s4
'14': audi_tt
'15': bmw_323i
'16': bmw_325i
'17': bmw_328i
'18': bmw_330ci
'19': bmw_330i
'20': bmw_335i
'21': bmw_525i
'22': bmw_528i
'23': bmw_530i
'24': bmw_535i
'25': bmw_540i
'26': bmw_545i
'27': bmw_550i
'28': bmw_740i
'29': bmw_745i
'30': bmw_750i
'31': bmw_m3
'32': bmw_m5
'33': bmw_x3
'34': bmw_x5
'35': bmw_z3
'36': bmw_z4
'37': buick_century
'38': buick_enclave
'39': buick_lacrosse
'40': buick_lesabre
'41': buick_lucerne
'42': buick_parkavenue
'43': buick_regal
'44': buick_rendezvous
'45': buick_riviera
'46': cadillac_catera
'47': cadillac_cts
'48': cadillac_deville
'49': cadillac_eldorado
'50': cadillac_escalade
'51': cadillac_seville
'52': cadillac_srx
'53': cadillac_sts
'54': chevrolet_astro
'55': chevrolet_avalanche
'56': chevrolet_aveo
'57': chevrolet_bel air
'58': chevrolet_blazer
'59': chevrolet_c-k1500
'60': chevrolet_c10
'61': chevrolet_camaro
'62': chevrolet_caprice
'63': chevrolet_cavalier
'64': chevrolet_chevelle
'65': chevrolet_cobalt
'66': chevrolet_colorado
'67': chevrolet_corvette
'68': chevrolet_cruze
'69': chevrolet_el camino
'70': chevrolet_equinox
'71': chevrolet_express
'72': chevrolet_hhr
'73': chevrolet_impala
'74': chevrolet_lumina
'75': chevrolet_malibu
'76': chevrolet_montecarlo
'77': chevrolet_nova
'78': chevrolet_prizm
'79': chevrolet_s10
'80': chevrolet_silverado
'81': chevrolet_sonic
'82': chevrolet_suburban
'83': chevrolet_tahoe
'84': chevrolet_tracker
'85': chevrolet_trailblazer
'86': chevrolet_traverse
'87': chevrolet_uplander
'88': chevrolet_venture
'89': chrysler_200
'90': chrysler_300
'91': chrysler_concorde
'92': chrysler_crossfire
'93': chrysler_pacifica
'94': chrysler_pt cruiser
'95': chrysler_sebring
'96': chrysler_town&country
'97': chrysler_voyager
'98': dodge_avenger
'99': dodge_caliber
'100': dodge_challenger
'101': dodge_charger
'102': dodge_dakota
'103': dodge_dart
'104': dodge_durango
'105': dodge_grand caravan
'106': dodge_intrepid
'107': dodge_journey
'108': dodge_magnum
'109': dodge_neon
'110': dodge_nitro
'111': dodge_ram
'112': dodge_stratus
'113': fiat_five hundred
'114': ford_bronco
'115': ford_contour
'116': ford_crown victoria
'117': ford_e150
'118': ford_e250
'119': ford_e350
'120': ford_edge
'121': ford_escape
'122': ford_escort
'123': ford_excursion
'124': ford_expedition
'125': ford_explorer
'126': ford_f100
'127': ford_f150
'128': ford_f250
'129': ford_f350
'130': ford_f450
'131': ford_fiesta
'132': ford_five hundred
'133': ford_focus
'134': ford_freestar
'135': ford_fusion
'136': ford_mustang
'137': ford_ranger
'138': ford_taurus
'139': ford_thunderbird
'140': ford_windstar
'141': gmc_acadia
'142': gmc_canyon
'143': gmc_envoy
'144': gmc_jimmy
'145': gmc_sierra
'146': gmc_sonoma
'147': gmc_suburban
'148': gmc_terrain
'149': gmc_yukon
'150': honda_accord
'151': honda_civic
'152': honda_cr-v
'153': honda_delsol
'154': honda_element
'155': honda_fit
'156': honda_odyssey
'157': honda_passport
'158': honda_pilot
'159': honda_prelude
'160': honda_ridgeline
'161': honda_s2000
'162': hummer_h2
'163': hummer_h3
'164': hyundai_accent
'165': hyundai_azera
'166': hyundai_elantra
'167': hyundai_genesis
'168': hyundai_santafe
'169': hyundai_sonata
'170': hyundai_tiburon
'171': hyundai_tucson
'172': infiniti_fx35
'173': infiniti_g35
'174': infiniti_g37
'175': infiniti_i30
'176': infiniti_i35
'177': infiniti_m35
'178': infiniti_q45
'179': infiniti_qx4
'180': infiniti_qx56
'181': isuzu_rodeo
'182': isuzu_trooper
'183': jaguar_s-type
'184': jaguar_x-type
'185': jaguar_xj
'186': jeep_cherokee
'187': jeep_cj5
'188': jeep_cj7
'189': jeep_commander
'190': jeep_compass
'191': jeep_grand
'192': jeep_liberty
'193': jeep_patriot
'194': jeep_wrangler
'195': kia_amanti
'196': kia_forte
'197': kia_optima
'198': kia_rio
'199': kia_sedona
'200': kia_sephia
'201': kia_sorento
'202': kia_soul
'203': kia_spectra
'204': kia_sportage
'205': landrover_discovery
'206': landrover_rangerover
'207': lexus_es300
'208': lexus_es330
'209': lexus_es350
'210': lexus_gs300
'211': lexus_gx470
'212': lexus_is250
'213': lexus_is300
'214': lexus_is350
'215': lexus_ls400
'216': lexus_ls430
'217': lexus_rx300
'218': lexus_rx330
'219': lexus_sc430
'220': lincoln_aviator
'221': lincoln_continental
'222': lincoln_ls
'223': lincoln_mark
'224': lincoln_mkx
'225': lincoln_mkz
'226': lincoln_navigator
'227': lincoln_towncar
'228': mazda_3
'229': mazda_5
'230': mazda_6
'231': mazda_626
'232': mazda_millenia
'233': mazda_mpv
'234': mazda_mx5
'235': mazda_protege
'236': mazda_rx7
'237': mazda_rx8
'238': mazda_tribute
'239': mercedes benz_c230
'240': mercedes benz_c240
'241': mercedes benz_c280
'242': mercedes benz_c300
'243': mercedes benz_c320
'244': mercedes benz_clk320
'245': mercedes benz_e320
'246': mercedes benz_e350
'247': mercedes benz_e500
'248': mercedes benz_ml320
'249': mercedes benz_ml350
'250': mercedes benz_ml500
'251': mercedes benz_s430
'252': mercedes benz_s500
'253': mercedes benz_s550
'254': mercedes benz_sl500
'255': mercury_cougar
'256': mercury_grandmarquis
'257': mercury_mariner
'258': mercury_milan
'259': mercury_mountaineer
'260': mercury_sable
'261': mercury_villager
'262': mini_cooper
'263': mitsubishi_3000gt
'264': mitsubishi_eclipse
'265': mitsubishi_endeavor
'266': mitsubishi_galant
'267': mitsubishi_lancer
'268': mitsubishi_mirage
'269': mitsubishi_montero
'270': mitsubishi_outlander
'271': nissan_240sx
'272': nissan_300zx
'273': nissan_350z
'274': nissan_altima
'275': nissan_armada
'276': nissan_frontier
'277': nissan_maxima
'278': nissan_murano
'279': nissan_pathfinder
'280': nissan_quest
'281': nissan_rogue
'282': nissan_sentra
'283': nissan_titan
'284': nissan_versa
'285': nissan_xterra
'286': oldsmobile_alero
'287': oldsmobile_aurora
'288': oldsmobile_bravada
'289': oldsmobile_cutlass
'290': oldsmobile_intrigue
'291': oldsmobile_silhouette
'292': plymouth_neon
'293': plymouth_voyager
'294': pontiac_bonneville
'295': pontiac_firebird
'296': pontiac_g5
'297': pontiac_g6
'298': pontiac_grandam
'299': pontiac_grandprix
'300': pontiac_gto
'301': pontiac_montana
'302': pontiac_sunfire
'303': pontiac_torrent
'304': pontiac_transam
'305': pontiac_vibe
'306': porsche_911
'307': porsche_boxster
'308': porsche_cayenne
'309': ram_1500
'310': saab_9-3
'311': saab_9-5
'312': saturn_aura
'313': saturn_ion
'314': saturn_l200
'315': saturn_l300
'316': saturn_sl1
'317': saturn_sl2
'318': saturn_vue
'319': scion_tc
'320': scion_xa
'321': scion_xb
'322': scion_xd
'323': smart_fortwo
'324': subaru_forester
'325': subaru_impreza
'326': subaru_legacy
'327': subaru_outback
'328': subaru_wrx
'329': suzuki_forenza
'330': suzuki_sx4
'331': suzuki_xl7
'332': toyota_4runner
'333': toyota_avalon
'334': toyota_camry
'335': toyota_celica
'336': toyota_corolla
'337': toyota_echo
'338': toyota_fjcruiser
'339': toyota_highlander
'340': toyota_landcruiser
'341': toyota_matrix
'342': toyota_mr2
'343': toyota_pickup
'344': toyota_prius
'345': toyota_rav4
'346': toyota_sequoia
'347': toyota_sienna
'348': toyota_solara
'349': toyota_supra
'350': toyota_t100
'351': toyota_tacoma
'352': toyota_tercel
'353': toyota_tundra
'354': toyota_yaris
'355': volkswagen_beetle
'356': volkswagen_bug
'357': volkswagen_cc
'358': volkswagen_eos
'359': volkswagen_golf
'360': volkswagen_gti
'361': volkswagen_jetta
'362': volkswagen_newbeetle
'363': volkswagen_passat
'364': volkswagen_rabbit
'365': volkswagen_touareg
'366': volvo_850
'367': volvo_c70
'368': volvo_s40
'369': volvo_s60
'370': volvo_s70
'371': volvo_s80
'372': volvo_v70
'373': volvo_xc70
'374': volvo_xc90
splits:
- name: train
num_bytes: 4490369111.482906
num_examples: 241664
download_size: 4489644227
dataset_size: 4490369111.482906
---
# Dataset Card for "VMMRdb_make_model_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_256 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 887459220.0
num_examples: 174285
download_size: 901755390
dataset_size: 887459220.0
---
# Dataset Card for "chunk_256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_pmlb_Hill_Valley_without_noise_sgosdt_l256_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 366867840
num_examples: 10000
- name: validation
num_bytes: 366877056
num_examples: 10000
download_size: 328595286
dataset_size: 733744896
---
# Dataset Card for "autotree_pmlb_Hill_Valley_without_noise_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liberatoratif/UK-Counties | ---
license: apache-2.0
---
|
zolak/twitter_dataset_78_1713117443 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 231178
num_examples: 584
download_size: 124976
dataset_size: 231178
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tessiw/german_OpenOrca_Format2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 6613611409
num_examples: 3983923
download_size: 3728509725
dataset_size: 6613611409
---
# Dataset Card for "german_OpenOrca_Format2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
benayas/snips_llm_v4 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 7164970
num_examples: 13084
- name: test
num_bytes: 768070
num_examples: 1400
download_size: 900859
dataset_size: 7933040
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
victorzarzu/interior-design-editing-prompts | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 469332
num_examples: 8833
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
--- |
liuyanchen1015/MULTI_VALUE_wnli_for_complementizer | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 6612
num_examples: 30
- name: test
num_bytes: 12768
num_examples: 45
- name: train
num_bytes: 46052
num_examples: 207
download_size: 30640
dataset_size: 65432
---
# Dataset Card for "MULTI_VALUE_wnli_for_complementizer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Rafa775/ramon | ---
license: openrail
---
|
rubertmi00/HealthCoachDataset | ---
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 1310787.5197472353
num_examples: 1000
- name: test
num_bytes: 348669.4802527646
num_examples: 266
download_size: 950973
dataset_size: 1659457.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
cestwc/SG-subzone-poi-sentiment | ---
dataset_info:
features:
- name: local_created_at
dtype: string
- name: id
dtype: int64
- name: text
dtype: string
- name: source
dtype: string
- name: truncated
dtype: bool
- name: in_reply_to_status_id
dtype: float64
- name: in_reply_to_user_id
dtype: float64
- name: user_id
dtype: int64
- name: user_name
dtype: string
- name: user_screen_name
dtype: string
- name: user_location
dtype: string
- name: user_url
dtype: string
- name: user_verified
dtype: bool
- name: user_default_profile
dtype: bool
- name: user_description
dtype: string
- name: user_followers_count
dtype: int64
- name: user_friends_count
dtype: int64
- name: user_listed_count
dtype: int64
- name: user_favourites_count
dtype: int64
- name: user_statuses_count
dtype: int64
- name: local_user_created_at
dtype: string
- name: place_id
dtype: string
- name: place_url
dtype: string
- name: place_place_type
dtype: string
- name: place_name
dtype: string
- name: place_country_code
dtype: string
- name: place_bounding_box_type
dtype: string
- name: place_bounding_box_coordinates
dtype: string
- name: is_quote_status
dtype: bool
- name: retweet_count
dtype: int64
- name: favorite_count
dtype: int64
- name: entities_hashtags
dtype: string
- name: entities_urls
dtype: string
- name: entities_symbols
dtype: string
- name: entities_user_mentions
dtype: string
- name: favorited
dtype: bool
- name: retweeted
dtype: bool
- name: possibly_sensitive
dtype: bool
- name: lang
dtype: string
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: year_created_at
dtype: int64
- name: month_created_at
dtype: int64
- name: day_created_at
dtype: int64
- name: weekday_created_at
dtype: int64
- name: hour_created_at
dtype: int64
- name: minute_created_at
dtype: int64
- name: year_user_created_at
dtype: int64
- name: month_user_created_at
dtype: int64
- name: day_user_created_at
dtype: int64
- name: weekday_user_created_at
dtype: int64
- name: hour_user_created_at
dtype: int64
- name: minute_user_created_at
dtype: int64
- name: subzone
dtype: string
- name: planning_area
dtype: string
- name: poi_flag
dtype: float64
- name: poi_id
dtype: string
- name: poi_dist
dtype: float64
- name: poi_latitude
dtype: float64
- name: poi_longitude
dtype: float64
- name: poi_name
dtype: string
- name: poi_type
dtype: string
- name: poi_cate2
dtype: string
- name: poi_cate3
dtype: string
- name: clean_text
dtype: string
- name: joy_score
dtype: float64
- name: trust_score
dtype: float64
- name: positive_score
dtype: float64
- name: sadness_score
dtype: float64
- name: disgust_score
dtype: float64
- name: anger_score
dtype: float64
- name: anticipation_score
dtype: float64
- name: negative_score
dtype: float64
- name: fear_score
dtype: float64
- name: surprise_score
dtype: float64
- name: words
dtype: string
- name: polarity_score
dtype: float64
- name: labels
dtype: int64
splits:
- name: '0203'
num_bytes: 1519418943
num_examples: 1025135
download_size: 415295950
dataset_size: 1519418943
---
# Dataset Card for "SG-subzone-poi-sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dog/unav-100 | ---
license: cc-by-4.0
dataset_info:
features:
- name: ytid
dtype: string
- name: start
dtype: float64
- name: end
dtype: float64
- name: duration
dtype: float64
- name: annotations
list:
- name: label
dtype: string
- name: label_id
dtype: int64
- name: segment_end
dtype: float64
- name: segment_start
dtype: float64
splits:
- name: train
num_bytes: 1044336
num_examples: 6489
- name: validation
num_bytes: 346495
num_examples: 2134
- name: test
num_bytes: 342199
num_examples: 2167
download_size: 709359
dataset_size: 1733030
---
|
open-llm-leaderboard/details_allknowingroger__M7-8B-passthrough | ---
pretty_name: Evaluation run of allknowingroger/M7-8B-passthrough
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [allknowingroger/M7-8B-passthrough](https://huggingface.co/allknowingroger/M7-8B-passthrough)\
\ 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_allknowingroger__M7-8B-passthrough\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-11T06:52:20.734020](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__M7-8B-passthrough/blob/main/results_2024-04-11T06-52-20.734020.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\": 0.6423348635921872,\n\
\ \"acc_stderr\": 0.03231625223252546,\n \"acc_norm\": 0.6446030902485047,\n\
\ \"acc_norm_stderr\": 0.03297304000372783,\n \"mc1\": 0.5924112607099143,\n\
\ \"mc1_stderr\": 0.01720194923455311,\n \"mc2\": 0.7379035451562636,\n\
\ \"mc2_stderr\": 0.014559397581751874\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6825938566552902,\n \"acc_stderr\": 0.013602239088038169,\n\
\ \"acc_norm\": 0.7167235494880546,\n \"acc_norm_stderr\": 0.013167478735134575\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7160924118701454,\n\
\ \"acc_stderr\": 0.004499710284381918,\n \"acc_norm\": 0.8863772156940849,\n\
\ \"acc_norm_stderr\": 0.0031670398072286784\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\
\ \"acc_stderr\": 0.04256193767901409,\n \"acc_norm\": 0.5851851851851851,\n\
\ \"acc_norm_stderr\": 0.04256193767901409\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n\
\ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \
\ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.028985455652334388,\n\
\ \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.028985455652334388\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\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\"\
: 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\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\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n\
\ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4417989417989418,\n \"acc_stderr\": 0.025576257061253833,\n \"\
acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.025576257061253833\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\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7645161290322581,\n \"acc_stderr\": 0.02413763242933771,\n \"\
acc_norm\": 0.7645161290322581,\n \"acc_norm_stderr\": 0.02413763242933771\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.46798029556650245,\n \"acc_stderr\": 0.03510766597959217,\n \"\
acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.03510766597959217\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\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139404,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139404\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124488,\n \"\
acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124488\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.02247325333276876,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.02247325333276876\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.29259259259259257,\n \"acc_stderr\": 0.027738969632176088,\n \
\ \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.027738969632176088\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059274,\n\
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059274\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3973509933774834,\n \"acc_stderr\": 0.0399552400768168,\n \"acc_norm\"\
: 0.3973509933774834,\n \"acc_norm_stderr\": 0.0399552400768168\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8330275229357799,\n\
\ \"acc_stderr\": 0.015990154885073368,\n \"acc_norm\": 0.8330275229357799,\n\
\ \"acc_norm_stderr\": 0.015990154885073368\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.5879629629629629,\n \"acc_stderr\": 0.03356787758160831,\n\
\ \"acc_norm\": 0.5879629629629629,\n \"acc_norm_stderr\": 0.03356787758160831\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"\
acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8270042194092827,\n \"acc_stderr\": 0.024621562866768424,\n \
\ \"acc_norm\": 0.8270042194092827,\n \"acc_norm_stderr\": 0.024621562866768424\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7130044843049327,\n\
\ \"acc_stderr\": 0.030360379710291954,\n \"acc_norm\": 0.7130044843049327,\n\
\ \"acc_norm_stderr\": 0.030360379710291954\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\
\ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.039849796533028725,\n \"\
acc_norm\": 0.743801652892562,\n \"acc_norm_stderr\": 0.039849796533028725\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\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\": 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909284\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\
\ \"acc_stderr\": 0.013547415658662253,\n \"acc_norm\": 0.8263090676883781,\n\
\ \"acc_norm_stderr\": 0.013547415658662253\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.02519018132760841,\n\
\ \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.02519018132760841\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3776536312849162,\n\
\ \"acc_stderr\": 0.01621414875213663,\n \"acc_norm\": 0.3776536312849162,\n\
\ \"acc_norm_stderr\": 0.01621414875213663\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.02633661346904664,\n\
\ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.02633661346904664\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\
\ \"acc_stderr\": 0.026236965881153273,\n \"acc_norm\": 0.6913183279742765,\n\
\ \"acc_norm_stderr\": 0.026236965881153273\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.0242885336377261,\n\
\ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.0242885336377261\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.02979071924382972,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.02979071924382972\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.49022164276401564,\n\
\ \"acc_stderr\": 0.012767793787729333,\n \"acc_norm\": 0.49022164276401564,\n\
\ \"acc_norm_stderr\": 0.012767793787729333\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.028064998167040094,\n\
\ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.028064998167040094\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.696078431372549,\n \"acc_stderr\": 0.01860755213127983,\n \
\ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.01860755213127983\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\
\ \"acc_stderr\": 0.02484575321230604,\n \"acc_norm\": 0.8557213930348259,\n\
\ \"acc_norm_stderr\": 0.02484575321230604\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\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\": 0.5924112607099143,\n\
\ \"mc1_stderr\": 0.01720194923455311,\n \"mc2\": 0.7379035451562636,\n\
\ \"mc2_stderr\": 0.014559397581751874\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8382004735595896,\n \"acc_stderr\": 0.010350128010292406\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5170583775587566,\n \
\ \"acc_stderr\": 0.013764467123761316\n }\n}\n```"
repo_url: https://huggingface.co/allknowingroger/M7-8B-passthrough
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|arc:challenge|25_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|arc:challenge|25_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|gsm8k|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|gsm8k|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hellaswag|10_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hellaswag|10_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T06-51-34.362826.parquet'
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- split: 2024_04_11T06_52_20.734020
path:
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- split: latest
path:
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T06-52-20.734020.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T06-52-20.734020.parquet'
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- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T06-52-20.734020.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T06-52-20.734020.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T06-51-34.362826.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T06-51-34.362826.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T06-51-34.362826.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_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_high_school_world_history_5
data_files:
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path:
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path:
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- split: latest
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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- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T06-51-34.362826.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T06-51-34.362826.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T06-51-34.362826.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T06-51-34.362826.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2024-04-11T06-51-34.362826.parquet'
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path:
- '**/details_harness|hendrycksTest-management|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-11T06-52-20.734020.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- '**/details_harness|winogrande|5_2024-04-11T06-51-34.362826.parquet'
- split: 2024_04_11T06_52_20.734020
path:
- '**/details_harness|winogrande|5_2024-04-11T06-52-20.734020.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-11T06-52-20.734020.parquet'
- config_name: results
data_files:
- split: 2024_04_11T06_51_34.362826
path:
- results_2024-04-11T06-51-34.362826.parquet
- split: 2024_04_11T06_52_20.734020
path:
- results_2024-04-11T06-52-20.734020.parquet
- split: latest
path:
- results_2024-04-11T06-52-20.734020.parquet
---
# Dataset Card for Evaluation run of allknowingroger/M7-8B-passthrough
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [allknowingroger/M7-8B-passthrough](https://huggingface.co/allknowingroger/M7-8B-passthrough) 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_allknowingroger__M7-8B-passthrough",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-11T06:52:20.734020](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__M7-8B-passthrough/blob/main/results_2024-04-11T06-52-20.734020.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": 0.6423348635921872,
"acc_stderr": 0.03231625223252546,
"acc_norm": 0.6446030902485047,
"acc_norm_stderr": 0.03297304000372783,
"mc1": 0.5924112607099143,
"mc1_stderr": 0.01720194923455311,
"mc2": 0.7379035451562636,
"mc2_stderr": 0.014559397581751874
},
"harness|arc:challenge|25": {
"acc": 0.6825938566552902,
"acc_stderr": 0.013602239088038169,
"acc_norm": 0.7167235494880546,
"acc_norm_stderr": 0.013167478735134575
},
"harness|hellaswag|10": {
"acc": 0.7160924118701454,
"acc_stderr": 0.004499710284381918,
"acc_norm": 0.8863772156940849,
"acc_norm_stderr": 0.0031670398072286784
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5851851851851851,
"acc_stderr": 0.04256193767901409,
"acc_norm": 0.5851851851851851,
"acc_norm_stderr": 0.04256193767901409
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.65,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6679245283018868,
"acc_stderr": 0.028985455652334388,
"acc_norm": 0.6679245283018868,
"acc_norm_stderr": 0.028985455652334388
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7430555555555556,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"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": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5957446808510638,
"acc_stderr": 0.03208115750788684,
"acc_norm": 0.5957446808510638,
"acc_norm_stderr": 0.03208115750788684
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4417989417989418,
"acc_stderr": 0.025576257061253833,
"acc_norm": 0.4417989417989418,
"acc_norm_stderr": 0.025576257061253833
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7645161290322581,
"acc_stderr": 0.02413763242933771,
"acc_norm": 0.7645161290322581,
"acc_norm_stderr": 0.02413763242933771
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.46798029556650245,
"acc_stderr": 0.03510766597959217,
"acc_norm": 0.46798029556650245,
"acc_norm_stderr": 0.03510766597959217
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
"acc_stderr": 0.03374402644139404,
"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.03374402644139404
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7525252525252525,
"acc_stderr": 0.030746300742124488,
"acc_norm": 0.7525252525252525,
"acc_norm_stderr": 0.030746300742124488
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.02247325333276876,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.02247325333276876
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
"acc_stderr": 0.023991500500313036,
"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.29259259259259257,
"acc_stderr": 0.027738969632176088,
"acc_norm": 0.29259259259259257,
"acc_norm_stderr": 0.027738969632176088
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6470588235294118,
"acc_stderr": 0.031041941304059274,
"acc_norm": 0.6470588235294118,
"acc_norm_stderr": 0.031041941304059274
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3973509933774834,
"acc_stderr": 0.0399552400768168,
"acc_norm": 0.3973509933774834,
"acc_norm_stderr": 0.0399552400768168
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8330275229357799,
"acc_stderr": 0.015990154885073368,
"acc_norm": 0.8330275229357799,
"acc_norm_stderr": 0.015990154885073368
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5879629629629629,
"acc_stderr": 0.03356787758160831,
"acc_norm": 0.5879629629629629,
"acc_norm_stderr": 0.03356787758160831
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8088235294117647,
"acc_stderr": 0.027599174300640766,
"acc_norm": 0.8088235294117647,
"acc_norm_stderr": 0.027599174300640766
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8270042194092827,
"acc_stderr": 0.024621562866768424,
"acc_norm": 0.8270042194092827,
"acc_norm_stderr": 0.024621562866768424
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7130044843049327,
"acc_stderr": 0.030360379710291954,
"acc_norm": 0.7130044843049327,
"acc_norm_stderr": 0.030360379710291954
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7480916030534351,
"acc_stderr": 0.03807387116306086,
"acc_norm": 0.7480916030534351,
"acc_norm_stderr": 0.03807387116306086
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.743801652892562,
"acc_stderr": 0.039849796533028725,
"acc_norm": 0.743801652892562,
"acc_norm_stderr": 0.039849796533028725
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
"acc_stderr": 0.047268355537191,
"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.047268355537191
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
"acc_stderr": 0.023086635086841407,
"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.023086635086841407
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.76,
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"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8263090676883781,
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"acc_norm": 0.8263090676883781,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.02519018132760841,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.02519018132760841
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3776536312849162,
"acc_stderr": 0.01621414875213663,
"acc_norm": 0.3776536312849162,
"acc_norm_stderr": 0.01621414875213663
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.696078431372549,
"acc_stderr": 0.02633661346904664,
"acc_norm": 0.696078431372549,
"acc_norm_stderr": 0.02633661346904664
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6913183279742765,
"acc_stderr": 0.026236965881153273,
"acc_norm": 0.6913183279742765,
"acc_norm_stderr": 0.026236965881153273
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7438271604938271,
"acc_stderr": 0.0242885336377261,
"acc_norm": 0.7438271604938271,
"acc_norm_stderr": 0.0242885336377261
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.475177304964539,
"acc_stderr": 0.02979071924382972,
"acc_norm": 0.475177304964539,
"acc_norm_stderr": 0.02979071924382972
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.49022164276401564,
"acc_stderr": 0.012767793787729333,
"acc_norm": 0.49022164276401564,
"acc_norm_stderr": 0.012767793787729333
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6911764705882353,
"acc_stderr": 0.028064998167040094,
"acc_norm": 0.6911764705882353,
"acc_norm_stderr": 0.028064998167040094
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.696078431372549,
"acc_stderr": 0.01860755213127983,
"acc_norm": 0.696078431372549,
"acc_norm_stderr": 0.01860755213127983
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
"acc_stderr": 0.045820048415054174,
"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8557213930348259,
"acc_stderr": 0.02484575321230604,
"acc_norm": 0.8557213930348259,
"acc_norm_stderr": 0.02484575321230604
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"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": 0.5924112607099143,
"mc1_stderr": 0.01720194923455311,
"mc2": 0.7379035451562636,
"mc2_stderr": 0.014559397581751874
},
"harness|winogrande|5": {
"acc": 0.8382004735595896,
"acc_stderr": 0.010350128010292406
},
"harness|gsm8k|5": {
"acc": 0.5170583775587566,
"acc_stderr": 0.013764467123761316
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
alvations/dslml24-jelly-submission-en | ---
dataset_info:
- config_name: dev
features:
- name: text
dtype: string
- name: label
dtype: string
- name: prediction_oneshot
dtype: string
- name: prediction_promptshot
dtype: string
- name: response_oneshot
list:
- name: generated_text
dtype: string
- name: response_promptshot
list:
- name: generated_text
dtype: string
- name: dataset
dtype: string
- name: split
dtype: string
- name: lang
dtype: string
splits:
- name: train
num_bytes: 1343303
num_examples: 599
download_size: 343580
dataset_size: 1343303
- config_name: test
features:
- name: text
dtype: string
- name: prediction_oneshot
dtype: string
- name: prediction_promptshot
dtype: string
- name: response_oneshot
list:
- name: generated_text
dtype: string
- name: response_promptshot
list:
- name: generated_text
dtype: string
- name: dataset
dtype: string
- name: split
dtype: string
- name: lang
dtype: string
splits:
- name: train
num_bytes: 673609
num_examples: 300
download_size: 183127
dataset_size: 673609
- config_name: train
features:
- name: text
dtype: string
- name: label
dtype: string
- name: prediction_oneshot
dtype: string
- name: prediction_promptshot
dtype: string
- name: response_oneshot
list:
- name: generated_text
dtype: string
- name: response_promptshot
list:
- name: generated_text
dtype: string
- name: dataset
dtype: string
- name: split
dtype: string
- name: lang
dtype: string
splits:
- name: train
num_bytes: 4828205
num_examples: 2097
download_size: 1261504
dataset_size: 4828205
configs:
- config_name: dev
data_files:
- split: train
path: dev/train-*
- config_name: test
data_files:
- split: train
path: test/train-*
- config_name: train
data_files:
- split: train
path: train/train-*
---
|
biznetgio/oasst2-indonesia | ---
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_count
dtype: int64
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: float64
- name: synthetic
dtype: bool
- name: model_name
dtype: 'null'
- name: detoxify
struct:
- name: identity_attack
dtype: float64
- name: insult
dtype: float64
- name: obscene
dtype: float64
- name: severe_toxicity
dtype: float64
- name: sexual_explicit
dtype: float64
- name: threat
dtype: float64
- name: toxicity
dtype: float64
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
struct:
- name: count
sequence: int64
- name: name
sequence: string
- name: labels
struct:
- name: count
sequence: int64
- name: name
sequence: string
- name: value
sequence: float64
splits:
- name: train
num_bytes: 42423357
num_examples: 39283
download_size: 14122340
dataset_size: 42423357
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
NimaBoscarino/fuego-20230224-002224-7dec99 | ---
tags:
- fuego
fuego:
id: 20230224-002224-7dec99
status: preparing
script: train.py
requirements_file: requirements.txt
space_id: NimaBoscarino/fuego-20230224-002224-7dec99
space_hardware: cpu-basic
---
|
income/cqadupstack-mathematica-top-20-gen-queries | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
---
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
yikeshu122/txttry | ---
license: bigscience-bloom-rail-1.0
---
|
Chapad0o/evilNeur | ---
license: openrail
---
|
Sultannn/id_recipe | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
task_ids:
- language-modeling
paperswithcode_id: null
pretty_name: Indonesian Recipe
---
# Dataset Card for id_recipe
## 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:** [Indonesian-recipe](https://github.com/sultanbst123/Hugging-Face-indo)
- **Repository:** [Indonesian-recipe](https://github.com/sultanbst123/Hugging-Face-indo)
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** [Sultan](sultansyach7@gmail.com)
### Dataset Summary
Indonesian foods are well-known for their rich taste. There are many spices used even for daily foods. This dataset may give insight on how to prepare Indonesian food.
id_recipe is an Indonesian Food Recipe dataset. The dataset contains >10000 Indonesian Recipe.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
### Data Splits
Here are the number of examples
| name |n.examples|
|-----------------|--------: |
| train | 14858 |
| val | 783 |
### Source Data
[here](https://www.kaggle.com/datasets/canggih/indonesian-food-recipes)
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### 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
MIT License
### Citation Information
[N/A]
### Contributions
Thanks to [@sultan](https://github.com/sultanbst123) for adding this dataset
|
japanese-asr/whisper_transcriptions.reazonspeech.all_54 | ---
dataset_info:
config_name: all
features:
- name: name
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 30447048045.0
num_examples: 268813
download_size: 30208509486
dataset_size: 30447048045.0
configs:
- config_name: all
data_files:
- split: train
path: all/train-*
---
|
DioulaD/MediaSpeechFrTest | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: test
num_bytes: 660047381.256
num_examples: 2498
download_size: 641637435
dataset_size: 660047381.256
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
hemangjoshi37a/token_classification_ratnakar_1300 | ---
license: mit
---
|
valurank/Adult-content-dataset | ---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for Adult_Content_Detection
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
850 Articles descriptions classified into two different categories namely: Adult, and Non_Adult
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of two columns namely Description and Category.
The Description column consists of the overview of the article and the Category column consists of the class each article belongs to
## Source Data
The dataset is scrapped across different platforms
|
open-llm-leaderboard/details_bigscience__bloomz-560m | ---
pretty_name: Evaluation run of bigscience/bloomz-560m
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 8 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_bigscience__bloomz-560m\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-04T12:37:15.813527](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-560m/blob/main/results_2023-12-04T12-37-15.813527.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\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/bigscience/bloomz-560m
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T02_59_38.387630
path:
- '**/details_harness|drop|3_2023-10-13T02-59-38.387630.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T02-59-38.387630.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T02_59_38.387630
path:
- '**/details_harness|gsm8k|5_2023-10-13T02-59-38.387630.parquet'
- split: 2023_12_03T14_34_05.520160
path:
- '**/details_harness|gsm8k|5_2023-12-03T14-34-05.520160.parquet'
- split: 2023_12_03T14_34_17.552843
path:
- '**/details_harness|gsm8k|5_2023-12-03T14-34-17.552843.parquet'
- split: 2023_12_03T15_36_24.223775
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-36-24.223775.parquet'
- split: 2023_12_03T15_36_26.532570
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-36-26.532570.parquet'
- split: 2023_12_04T09_27_25.322225
path:
- '**/details_harness|gsm8k|5_2023-12-04T09-27-25.322225.parquet'
- split: 2023_12_04T12_37_10.556639
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-37-10.556639.parquet'
- split: 2023_12_04T12_37_15.813527
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-37-15.813527.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-37-15.813527.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T02_59_38.387630
path:
- '**/details_harness|winogrande|5_2023-10-13T02-59-38.387630.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T02-59-38.387630.parquet'
- config_name: results
data_files:
- split: 2023_10_13T02_59_38.387630
path:
- results_2023-10-13T02-59-38.387630.parquet
- split: 2023_12_03T14_34_05.520160
path:
- results_2023-12-03T14-34-05.520160.parquet
- split: 2023_12_03T14_34_17.552843
path:
- results_2023-12-03T14-34-17.552843.parquet
- split: 2023_12_03T15_36_24.223775
path:
- results_2023-12-03T15-36-24.223775.parquet
- split: 2023_12_03T15_36_26.532570
path:
- results_2023-12-03T15-36-26.532570.parquet
- split: 2023_12_04T09_27_25.322225
path:
- results_2023-12-04T09-27-25.322225.parquet
- split: 2023_12_04T12_37_10.556639
path:
- results_2023-12-04T12-37-10.556639.parquet
- split: 2023_12_04T12_37_15.813527
path:
- results_2023-12-04T12-37-15.813527.parquet
- split: latest
path:
- results_2023-12-04T12-37-15.813527.parquet
---
# Dataset Card for Evaluation run of bigscience/bloomz-560m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigscience/bloomz-560m
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 8 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_bigscience__bloomz-560m",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T12:37:15.813527](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-560m/blob/main/results_2023-12-04T12-37-15.813527.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": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### 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] |
nyu-mll/blimp | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
paperswithcode_id: blimp
pretty_name: BLiMP
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dtype: bool
- name: lexically_identical
dtype: bool
- name: pair_id
dtype: int32
splits:
- name: train
num_bytes: 247039
num_examples: 1000
download_size: 95504
dataset_size: 247039
- config_name: wh_vs_that_with_gap
features:
- name: sentence_good
dtype: string
- name: sentence_bad
dtype: string
- name: field
dtype: string
- name: linguistics_term
dtype: string
- name: UID
dtype: string
- name: simple_LM_method
dtype: bool
- name: one_prefix_method
dtype: bool
- name: two_prefix_method
dtype: bool
- name: lexically_identical
dtype: bool
- name: pair_id
dtype: int32
splits:
- name: train
num_bytes: 173386
num_examples: 1000
download_size: 60291
dataset_size: 173386
- config_name: wh_vs_that_with_gap_long_distance
features:
- name: sentence_good
dtype: string
- name: sentence_bad
dtype: string
- name: field
dtype: string
- name: linguistics_term
dtype: string
- name: UID
dtype: string
- name: simple_LM_method
dtype: bool
- name: one_prefix_method
dtype: bool
- name: two_prefix_method
dtype: bool
- name: lexically_identical
dtype: bool
- name: pair_id
dtype: int32
splits:
- name: train
num_bytes: 231595
num_examples: 1000
download_size: 84147
dataset_size: 231595
configs:
- config_name: adjunct_island
data_files:
- split: train
path: adjunct_island/train-*
- config_name: anaphor_gender_agreement
data_files:
- split: train
path: anaphor_gender_agreement/train-*
- config_name: anaphor_number_agreement
data_files:
- split: train
path: anaphor_number_agreement/train-*
- config_name: animate_subject_passive
data_files:
- split: train
path: animate_subject_passive/train-*
- config_name: animate_subject_trans
data_files:
- split: train
path: animate_subject_trans/train-*
- config_name: causative
data_files:
- split: train
path: causative/train-*
- config_name: complex_NP_island
data_files:
- split: train
path: complex_NP_island/train-*
- config_name: coordinate_structure_constraint_complex_left_branch
data_files:
- split: train
path: coordinate_structure_constraint_complex_left_branch/train-*
- config_name: coordinate_structure_constraint_object_extraction
data_files:
- split: train
path: coordinate_structure_constraint_object_extraction/train-*
- config_name: determiner_noun_agreement_1
data_files:
- split: train
path: determiner_noun_agreement_1/train-*
- config_name: determiner_noun_agreement_2
data_files:
- split: train
path: determiner_noun_agreement_2/train-*
- config_name: determiner_noun_agreement_irregular_1
data_files:
- split: train
path: determiner_noun_agreement_irregular_1/train-*
- config_name: determiner_noun_agreement_irregular_2
data_files:
- split: train
path: determiner_noun_agreement_irregular_2/train-*
- config_name: determiner_noun_agreement_with_adj_2
data_files:
- split: train
path: determiner_noun_agreement_with_adj_2/train-*
- config_name: determiner_noun_agreement_with_adj_irregular_1
data_files:
- split: train
path: determiner_noun_agreement_with_adj_irregular_1/train-*
- config_name: determiner_noun_agreement_with_adj_irregular_2
data_files:
- split: train
path: determiner_noun_agreement_with_adj_irregular_2/train-*
- config_name: determiner_noun_agreement_with_adjective_1
data_files:
- split: train
path: determiner_noun_agreement_with_adjective_1/train-*
- config_name: distractor_agreement_relational_noun
data_files:
- split: train
path: distractor_agreement_relational_noun/train-*
- config_name: distractor_agreement_relative_clause
data_files:
- split: train
path: distractor_agreement_relative_clause/train-*
- config_name: drop_argument
data_files:
- split: train
path: drop_argument/train-*
- config_name: ellipsis_n_bar_1
data_files:
- split: train
path: ellipsis_n_bar_1/train-*
- config_name: ellipsis_n_bar_2
data_files:
- split: train
path: ellipsis_n_bar_2/train-*
- config_name: existential_there_object_raising
data_files:
- split: train
path: existential_there_object_raising/train-*
- config_name: existential_there_quantifiers_1
data_files:
- split: train
path: existential_there_quantifiers_1/train-*
- config_name: existential_there_quantifiers_2
data_files:
- split: train
path: existential_there_quantifiers_2/train-*
- config_name: existential_there_subject_raising
data_files:
- split: train
path: existential_there_subject_raising/train-*
- config_name: expletive_it_object_raising
data_files:
- split: train
path: expletive_it_object_raising/train-*
- config_name: inchoative
data_files:
- split: train
path: inchoative/train-*
- config_name: intransitive
data_files:
- split: train
path: intransitive/train-*
- config_name: irregular_past_participle_adjectives
data_files:
- split: train
path: irregular_past_participle_adjectives/train-*
- config_name: irregular_past_participle_verbs
data_files:
- split: train
path: irregular_past_participle_verbs/train-*
- config_name: irregular_plural_subject_verb_agreement_1
data_files:
- split: train
path: irregular_plural_subject_verb_agreement_1/train-*
- config_name: irregular_plural_subject_verb_agreement_2
data_files:
- split: train
path: irregular_plural_subject_verb_agreement_2/train-*
- config_name: left_branch_island_echo_question
data_files:
- split: train
path: left_branch_island_echo_question/train-*
- config_name: left_branch_island_simple_question
data_files:
- split: train
path: left_branch_island_simple_question/train-*
- config_name: matrix_question_npi_licensor_present
data_files:
- split: train
path: matrix_question_npi_licensor_present/train-*
- config_name: npi_present_1
data_files:
- split: train
path: npi_present_1/train-*
- config_name: npi_present_2
data_files:
- split: train
path: npi_present_2/train-*
- config_name: only_npi_licensor_present
data_files:
- split: train
path: only_npi_licensor_present/train-*
- config_name: only_npi_scope
data_files:
- split: train
path: only_npi_scope/train-*
- config_name: passive_1
data_files:
- split: train
path: passive_1/train-*
- config_name: passive_2
data_files:
- split: train
path: passive_2/train-*
- config_name: principle_A_c_command
data_files:
- split: train
path: principle_A_c_command/train-*
- config_name: principle_A_case_1
data_files:
- split: train
path: principle_A_case_1/train-*
- config_name: principle_A_case_2
data_files:
- split: train
path: principle_A_case_2/train-*
- config_name: principle_A_domain_1
data_files:
- split: train
path: principle_A_domain_1/train-*
- config_name: principle_A_domain_2
data_files:
- split: train
path: principle_A_domain_2/train-*
- config_name: principle_A_domain_3
data_files:
- split: train
path: principle_A_domain_3/train-*
- config_name: principle_A_reconstruction
data_files:
- split: train
path: principle_A_reconstruction/train-*
- config_name: regular_plural_subject_verb_agreement_1
data_files:
- split: train
path: regular_plural_subject_verb_agreement_1/train-*
- config_name: regular_plural_subject_verb_agreement_2
data_files:
- split: train
path: regular_plural_subject_verb_agreement_2/train-*
- config_name: sentential_negation_npi_licensor_present
data_files:
- split: train
path: sentential_negation_npi_licensor_present/train-*
- config_name: sentential_negation_npi_scope
data_files:
- split: train
path: sentential_negation_npi_scope/train-*
- config_name: sentential_subject_island
data_files:
- split: train
path: sentential_subject_island/train-*
- config_name: superlative_quantifiers_1
data_files:
- split: train
path: superlative_quantifiers_1/train-*
- config_name: superlative_quantifiers_2
data_files:
- split: train
path: superlative_quantifiers_2/train-*
- config_name: tough_vs_raising_1
data_files:
- split: train
path: tough_vs_raising_1/train-*
- config_name: tough_vs_raising_2
data_files:
- split: train
path: tough_vs_raising_2/train-*
- config_name: transitive
data_files:
- split: train
path: transitive/train-*
- config_name: wh_island
data_files:
- split: train
path: wh_island/train-*
- config_name: wh_questions_object_gap
data_files:
- split: train
path: wh_questions_object_gap/train-*
- config_name: wh_questions_subject_gap
data_files:
- split: train
path: wh_questions_subject_gap/train-*
- config_name: wh_questions_subject_gap_long_distance
data_files:
- split: train
path: wh_questions_subject_gap_long_distance/train-*
- config_name: wh_vs_that_no_gap
data_files:
- split: train
path: wh_vs_that_no_gap/train-*
- config_name: wh_vs_that_no_gap_long_distance
data_files:
- split: train
path: wh_vs_that_no_gap_long_distance/train-*
- config_name: wh_vs_that_with_gap
data_files:
- split: train
path: wh_vs_that_with_gap/train-*
- config_name: wh_vs_that_with_gap_long_distance
data_files:
- split: train
path: wh_vs_that_with_gap_long_distance/train-*
---
# Dataset Card for "blimp"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/alexwarstadt/blimp
- **Paper:** [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://doi.org/10.1162/tacl_a_00321)
- **Paper:** https://arxiv.org/abs/1912.00582
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 29.58 MB
- **Size of the generated dataset:** 11.45 MB
- **Total amount of disk used:** 41.03 MB
### Dataset Summary
BLiMP is a challenge set for evaluating what language models (LMs) know about
major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each
containing 1000 minimal pairs isolating specific contrasts in syntax,
morphology, or semantics. The data is automatically generated according to
expert-crafted grammars.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### adjunct_island
- **Size of downloaded dataset files:** 0.36 MB
- **Size of the generated dataset:** 0.17 MB
- **Total amount of disk used:** 0.52 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
#### anaphor_gender_agreement
- **Size of downloaded dataset files:** 0.44 MB
- **Size of the generated dataset:** 0.14 MB
- **Total amount of disk used:** 0.57 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
#### anaphor_number_agreement
- **Size of downloaded dataset files:** 0.45 MB
- **Size of the generated dataset:** 0.14 MB
- **Total amount of disk used:** 0.59 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
#### animate_subject_passive
- **Size of downloaded dataset files:** 0.46 MB
- **Size of the generated dataset:** 0.15 MB
- **Total amount of disk used:** 0.61 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
#### animate_subject_trans
- **Size of downloaded dataset files:** 0.43 MB
- **Size of the generated dataset:** 0.13 MB
- **Total amount of disk used:** 0.57 MB
An example of 'train' looks as follows.
```
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
```
### Data Fields
The data fields are the same among all splits.
#### adjunct_island
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
#### anaphor_gender_agreement
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
#### anaphor_number_agreement
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
#### animate_subject_passive
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
#### animate_subject_trans
- `sentence_good`: a `string` feature.
- `sentence_bad`: a `string` feature.
- `field`: a `string` feature.
- `linguistics_term`: a `string` feature.
- `UID`: a `string` feature.
- `simple_LM_method`: a `bool` feature.
- `one_prefix_method`: a `bool` feature.
- `two_prefix_method`: a `bool` feature.
- `lexically_identical`: a `bool` feature.
- `pair_id`: a `int32` feature.
### Data Splits
| name |train|
|------------------------|----:|
|adjunct_island | 1000|
|anaphor_gender_agreement| 1000|
|anaphor_number_agreement| 1000|
|animate_subject_passive | 1000|
|animate_subject_trans | 1000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
BLiMP is distributed under a [CC-BY](https://creativecommons.org/licenses/by/4.0/) license. Source: https://github.com/alexwarstadt/blimp#license
### Citation Information
```
@article{warstadt2020blimp,
author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.},
title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English},
journal = {Transactions of the Association for Computational Linguistics},
volume = {8},
number = {},
pages = {377-392},
year = {2020},
doi = {10.1162/tacl\_a\_00321},
URL = {https://doi.org/10.1162/tacl_a_00321},
eprint = {https://doi.org/10.1162/tacl_a_00321},
abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
}
```
#### Errata
Some results were misreported in the published TACL version. Please refer to the corrected version on arXiv: https://arxiv.org/abs/1912.00582
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
isixhosa_ner_corpus | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- xh
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: IsixhosaNerCorpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: isixhosa_ner_corpus
splits:
- name: train
num_bytes: 2414995
num_examples: 6284
download_size: 14513302
dataset_size: 2414995
---
# Dataset Card for [Dataset Name]
## 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:** [IsiXhosa Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/312)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
### Dataset Summary
The isiXhosa Ner Corpus is a Xhosa dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Xhosa language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Xhosa.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [7, 8, 5, 6, 0],
'tokens': ['Injongo', 'ye-website', 'yaseMzantsi', 'Afrika', 'kukuvelisa']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Xhosa.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
[More Information Needed]
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[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
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{isixhosa_ner_corpus,
author = { K. Podile and
Roald Eiselen},
title = {NCHLT isiXhosa Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/312},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. |
swaroopajit/next-dataset-refined-batch-10000 | ---
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 326024308.0
num_examples: 1000
download_size: 299977034
dataset_size: 326024308.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "next-dataset-refined-batch-10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Camillahannesbo/neuro_patents_bds | ---
dataset_info:
features:
- name: appln_id
dtype: int64
- name: appln_filing_date
dtype: string
- name: docdb_family_id
dtype: int64
- name: granted
dtype: string
- name: appln_abstract
dtype: string
- name: appln_abstract_lg
dtype: string
- name: appln_title
dtype: string
- name: applt_coun
dtype: string
- name: invt_coun
dtype: string
- name: cpc
dtype: string
- name: ipc
sequence: string
- name: __index_level_0__
dtype: int64
- name: input
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 13312.2
num_examples: 6
download_size: 27477
dataset_size: 13312.2
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961035 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b1
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: mismatch
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-1b1
* Dataset: phpthinh/examplei
* Config: mismatch
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model. |
tara-jew/mini-platypus | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 30738175.902270723
num_examples: 24895
download_size: 15478591
dataset_size: 30738175.902270723
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
KonstantyM/science_qa_input_label_prep | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: context
dtype: string
- name: input
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 14836491177
num_examples: 4281664
download_size: 8551603528
dataset_size: 14836491177
---
# Dataset Card for "science_qa_input_label_prep"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Jotschi/german-news-titles | ---
language:
- de
license_name: apache-2.0
license_link: https://www.apache.org/licenses/LICENSE-2.0
tags:
- german
- synthetic
annotations_creators:
- machine-generated
pretty_name: German News Titles
size_categories:
- n<1k
task_categories:
- text-generation
- summarization
---
# Dataset Card for German News Titles
The dataset contains synthetically generated german news articles and a set of corresponding titles.
## Dataset Description
- **Curated by:** Jotschi
- **Language(s) (NLP):** German
- **License:** Apache 2.0
## Dataset Creation
The dataset was created using `dolphin-mixtral:v2.7`. The [source scripts](https://github.com/Jotschi/llm-experiments/tree/master/summarization) generated a news article based on a given topic. For the resulting article multiple titles were generated which are included in the dataset.
|
Kingmex/EricMartin | ---
license: apache-2.0
---
|
mwitiderrick/gsm8k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4060351
num_examples: 7473
download_size: 2169417
dataset_size: 4060351
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
```
Question:{question}
Answer: {answer}
``` |
anhaltai/fincorpus-de-10k | ---
language:
- en
- de
tags:
- financial
- bilingual
- pdf
pretty_name: FinCorpus-DE10k
annotations_creators:
- no-annotation
language_creators:
- found
size_categories:
- 10K<n<100K
license: cc-by-nc-nd-4.0
dataset_info:
- config_name: BBK_monthly
features:
- name: filename
dtype: string
- name: text
dtype: string
splits:
- name: train
download_size: 271752073
dataset_size: 0
- config_name: Law
features:
- name: filename
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 25707085
num_examples: 134
download_size: 271752073
dataset_size: 25707085
- config_name: all
features:
- name: filename
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 946487016
num_examples: 10402
download_size: 271752073
dataset_size: 946487016
- config_name: Annual_reports
features:
- name: filename
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 54268688
num_examples: 87
download_size: 271752073
dataset_size: 54268688
- config_name: Final_terms
features:
- name: filename
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 601219720
num_examples: 9591
download_size: 271752073
dataset_size: 601219720
- config_name: Base_prospectuses
features:
- name: filename
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 265291523
num_examples: 590
download_size: 271752073
dataset_size: 265291523
---
# Dataset Card for FinCorpus-DE10k
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
FinCorpus-DE10k is a corpus containing 12,235 PDF files of financial documents, mostly security prospectuses, along with plaintext files for approximately 10,500 of these documents. The documents are primarily in German (71%), with the rest being bilingual (German and English). This dataset aims to facilitate tasks like text analysis, language modeling, and document understanding in the financial domain.
This dataset is a subset of the above dataset, with the collections we felt comfortable releasing under permissive CC licenses. It omits the IFRS (containing 7 documents) and Informational_materials (127/129 txt/pdf files) collections. To get access to the full corpus, get in touch with us.
- **Curated by:** Nata Kozaeva, Serhii Hamotskyi, Christian Hänig
- **Language(s) (NLP):** German (DE), Bilingual (German and English)
- **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) except the monthly and annual reports, which are [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/).
It's composed of multiple collections, with the text content available as dataset configs as:
- Annual_reports
- BBK_monthly
- Base_prospectuses
- Final_terms
- Law
(By default, all collections are downloaded).
The entire corpus, pdf and txt files, can be downloaded here: [https://huggingface.co/datasets/anhaltai/fincorpus-de-10k/resolve/main/data/corpus_safe.zip?download=true](https://huggingface.co/datasets/anhaltai/fincorpus-de-10k/resolve/main/data/corpus_safe.zip?download=true)
### Dataset Sources
The FinCorpus-DE10k dataset is composed of financial documents from various collections, each with its unique characteristics and source of origin. The documents were primarily sourced from the websites of financial institutions, regulatory bodies, and publicly available databases, with significant contributions from the Deutsche Bundesbank. The dataset includes:
- **Final Terms Prospectuses**: These documents detail the terms and conditions of the issuance of financial securities, predominantly collected by the Deutsche Bundesbank. They form the largest part of the dataset, with documents ranging from 1 to 719 pages, but mainly under 100 pages.
- **Base Prospectuses**: Containing information about the issuer, description of the security, and the summary of the prospectus. These documents are longer and fewer compared to the Final Terms but hold comprehensive information required for investors.
- **Bundesbank Monthly Reports**: Consisting of 838 monthly reports from the German Bundesbank, spanning from 1949 to 2022. These documents offer a historical perspective on the German financial language. We didn't extract text from these documents. **Licensed [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)**
- **Annual Reports**: This collection includes annual (and some quarterly) reports from the Bundesbank and other institutions, covering economic and financial issues, monetary policy, and financial stability risks. **Licensed [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)**
- **Law**: Featuring German laws in the financial and related domains, including some English translations. This collection reflects the regulations applicable to the financial sector in Germany and EU Directives implemented into German law.
The collection as a whole is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) except where stated otherwise.
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/AnhaltAI/fincorpus-de-10k-scripts/
- **Paper:** **TODO**
## Uses
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
By providing a rich collection of financial documents in PDF format, the dataset facilitates the development of algorithms that can navigate the complex layouts typically found in financial documents.
FinCorpus-DE10k is also suited for developing and testing NLP models specialized in the financial domain, including but not limited to information extraction, named entity recognition, and specialized language models.
<!--
### Out-of-Scope Use
// This section addresses misuse, malicious use, and uses that the dataset will not work well for.
The dataset is not designed for non-NLP tasks or NLP tasks outside the financial domain.
-->
## 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. -->
When used through `load_dataset()`, the dataset has two features: `filename` and `text`, one instance per .txt document.
The complete dataset, pdf and txt, can be found in [corpus.zip](https://huggingface.co/datasets/anhaltai/fincorpus-de-10k/resolve/main/data/corpus_safe.zip?download=true). In the archive, `metadata.csv` contains the path for the PDF and its extracted .txt (if available), as well as collection name, presence of extracted text, paths to PDF and .txt files, document language(s), and financial identifiers like ISIN and country for relevant documents.
The pdf and txt subfolders contain the same mirrored directory structure, sorted by collection.
## Dataset Creation
<!--
### Curation Rationale
// Motivation for the creation of this dataset.
Created to support research in financial document analysis, facilitating advancements in financial technology, regulatory compliance, and economic research.
-->
### Source Data
#### 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. -->
Extensive preprocessing was applied to ensure the quality and uniformity of the dataset. It's described in our paper: **TODO**
#### 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 documents were produced by various financial institutions, regulatory bodies, companies, and the Deutsche Bundesbank.
#### 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. -->
The dataset contains financial documents that are publicly available.
#### Licensing
We diligently adhered to the licensing guidelines to the best of our understanding. However, the responsibility for the use of the documents and compliance with applicable laws rests with you.
Get in touch with us if any of the documents need to be removed from the collection.
Relevant links are:
- Bundesbank monthly+annual allows using its documents if they are unchanged, hence the CC BY-NC-ND license: [Nutzungsbedingungen - Für den allgemeinen Gebrauch der Website \| Deutsche Bundesbank](https://www.bundesbank.de/de/startseite/benutzerhinweise/nutzungsbedingungen-fuer-den-allgemeinen-gebrauch-der-website-763554#tar-4)
- German laws are public domain: [Act on Copyright and Related Rights (Urheberrechtsgesetz – UrhG)](https://www.gesetze-im-internet.de/englisch_urhg/englisch_urhg.html#p0037)
- Final terms documents (and their Basisprospekte) can be considered public domain (at least their textual content), since the relevant EU regulation _mandates_ they are published and freely accessible: [L_2017168EN.01001201.xml](https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX%3A32017R1129)
## Citation
Temporary citation until paper is published:
```
@inproceedings{hamotskyi-etal-2024-fincorpus,
title = {{{FinCorpus-DE10k}}: {{A}} Corpus for the German Financial Domain},
booktitle = {The 2024 {{Joint International Conference}} on {{Computational Linguistics}}, {{Language Resources}} and {{Evaluation}} ({{LREC-COLING}} 2024)},
author = {Hamotskyi, Serhii and Kozaeva, Nata and H{\"a}nig, Christian},
year = {2024},
month = may,
publisher = {European Language Resources Association},
address = {Torino, Italy},
abstract = {We introduce a predominantly German corpus comprising 12.5k PDF documents sourced from the financial domain. The corresponding extracted textual data encompasses more than 165 million tokens derived predominantly from German, and to a lesser extent, bilingual documents. We provide detailed information about the document types included in the corpus, such as final terms, base prospectuses, annual reports, information materials, law documents, international financial reporting standards, and monthly reports from the Bundesbank, accompanied by comprehensive statistical analysis. To our knowledge, it is the first non-email German financial corpus available, and we hope it will fill this gap and foster further research in the financial domain both in the German language and in multilingual contexts.}
}
```
|
Tumbal123/tumbal1 | ---
dataset_info:
features:
- name: created_at;id_str;full_text;quote_count;reply_count;retweet_count;favorite_count;lang;user_id_str;conversation_id_str;username;tweet_url
dtype: string
splits:
- name: train
num_bytes: 28788.9
num_examples: 112
- name: test
num_bytes: 12338.1
num_examples: 48
download_size: 27515
dataset_size: 41127.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
taufeeque/othellogpt_old | ---
dataset_info:
features:
- name: tokens
sequence: int64
splits:
- name: train
num_bytes: 9676052584
num_examples: 20000000
- name: validation
num_bytes: 1836463376
num_examples: 3796010
download_size: 1026466555
dataset_size: 11512515960
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
biglam/bnl_ground_truth_newspapers_before_1878 | ---
license: cc0-1.0
---
### Dataset description
33.000 transcribed text lines from historical newspapers (before 1878) along with the cropped images of the original scans
Text line based OCR
19.000 text lines in Antiqua
14.000 text lines in Fraktur
Transcribed using double-keying (99.95% accuracy)
Public Domain, CC0 (See copyright notice)
Best for training an OCR engine
The newspapers used are:
- Le Gratis luxembourgeois (1857-1858)
- Luxemburger Volks-Freund (1869-1876)
- L'Arlequin (1848-1848)
- Courrier du Grand-Duché de Luxembourg (1844-1868)
- L'Avenir (1868-1871)
- Der Wächter an der Sauer (1849-1869)
- Luxemburger Zeitung (1844-1845)
- Luxemburger Zeitung = Journal de Luxembourg (1858-1859)
- Der Volksfreund (1848-1849)
- Cäcilia (1862-1871)
- Kirchlicher Anzeiger für die Diözese Luxemburg (1871-1878)
- L'Indépendance luxembourgeoise (1871-1878)
- Luxemburger Anzeiger (1856)
- L'Union (1860-1871)
- Diekircher Wochenblatt (1837-1848)
- Das Vaterland (1869-1870)
- D'Wäschfra (1868-1878)
- Luxemburger Bauernzeitung (1857)
- Luxemburger Wort (1848-1878)
### URL for this dataset
https://data.bnl.lu/data/historical-newspapers/
### Dataset format
Two JSONL files (antiqua.jsonl.gz and fraktur.jsonl.gz) with the follwing fields:
- `font` is either antiqua or fraktur
- `img` is the filename of the associated image for the text
- `text` is the handcorrected double-keyed text transcribed from the image
Sample:
```json
{
"font": "fraktur",
"img": "fraktur-000011.png",
"text": "Vidal die Vollmacht für Paris an. Auch"
}
```
In addition there are two `.zip` files with the associated images
### Dataset modality
Text and associated Images from Scans
### Dataset licence
Creative Commons Public Domain Dedication and Certification
### size of dataset
500MB-2GB
### Contact details for data custodian
opendata@bnl.etat.lu
|
yfyeung/icefall-ssl-librispeech-pretrain | ---
license: apache-2.0
---
|
ian-m/processed_bert_dataset-datalore | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 24902388000.0
num_examples: 6917330
download_size: 6083242697
dataset_size: 24902388000.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "processed_bert_dataset-datalore"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_rte_after_perfect | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 320900
num_examples: 723
- name: train
num_bytes: 279074
num_examples: 588
download_size: 385312
dataset_size: 599974
---
# Dataset Card for "MULTI_VALUE_rte_after_perfect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-92e227-2073967129 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: it5/mt5-base-news-summarization
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: it5/mt5-base-news-summarization
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mtharrison](https://huggingface.co/mtharrison) for evaluating this model. |
datahrvoje/twitter_dataset_1713021527 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 35299
num_examples: 88
download_size: 17876
dataset_size: 35299
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/fujiwara_no_mokou_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of fujiwara_no_mokou/藤原妹紅/후지와라노모코 (Touhou)
This is the dataset of fujiwara_no_mokou/藤原妹紅/후지와라노모코 (Touhou), containing 500 images and their tags.
The core tags of this character are `long_hair, bow, hair_bow, red_eyes, very_long_hair, white_hair, ribbon, hair_ribbon, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 789.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujiwara_no_mokou_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 450.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujiwara_no_mokou_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1163 | 895.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujiwara_no_mokou_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 700.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fujiwara_no_mokou_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1163 | 1.23 GiB | [Download](https://huggingface.co/datasets/CyberHarem/fujiwara_no_mokou_touhou/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/fujiwara_no_mokou_touhou',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, solo, suspenders, fire, pants, grey_hair |
| 1 | 9 |  |  |  |  |  | 1girl, fire, solo, suspenders, pants, shirt, grin |
| 2 | 20 |  |  |  |  |  | 1girl, solo, suspenders, white_bow, white_shirt, looking_at_viewer, collared_shirt, red_pants, simple_background, closed_mouth, grey_hair, white_background, hair_between_eyes, fire, juliet_sleeves, breasts, upper_body, buttons, ofuda_on_clothes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | suspenders | fire | pants | grey_hair | shirt | grin | white_bow | white_shirt | looking_at_viewer | collared_shirt | red_pants | simple_background | closed_mouth | white_background | hair_between_eyes | juliet_sleeves | breasts | upper_body | buttons | ofuda_on_clothes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------|:-------|:--------|:------------|:--------|:-------|:------------|:--------------|:--------------------|:-----------------|:------------|:--------------------|:---------------|:-------------------|:--------------------|:-----------------|:----------|:-------------|:----------|:-------------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | | X | X | | | | | | | | | | | | | | |
| 2 | 20 |  |  |  |  |  | X | X | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
ahishamm/HAM_db_enhanced_balanced | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': akiec
'1': bcc
'2': bkl
'3': df
'4': mel
'5': nv
'6': vasc
splits:
- name: train
num_bytes: 2808030092.924
num_examples: 43449
- name: test
num_bytes: 459957991.57
num_examples: 9387
download_size: 3182084216
dataset_size: 3267988084.494
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
akadhim-ai/dilbert-comic-dataset | ---
license: openrail
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': train
- name: text
dtype: string
splits:
- name: train
num_bytes: 1846493.0
num_examples: 50
download_size: 0
dataset_size: 1846493.0
---
|
eunbinni/ola_llama2_13B_t3_data | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 5297963
num_examples: 34771
download_size: 3300876
dataset_size: 5297963
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ola_llama2_13B_t3_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-one-sec-cv12/chunk_19 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1433815544
num_examples: 281582
download_size: 1460335757
dataset_size: 1433815544
---
# Dataset Card for "chunk_19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Jing24/new_sorted_generate_sub_0 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: conf
dtype: float32
splits:
- name: train
num_bytes: 71844161
num_examples: 78391
download_size: 13243852
dataset_size: 71844161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
benayas/banking_llm_v5 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 28215739
num_examples: 10003
- name: test
num_bytes: 8667330
num_examples: 3080
download_size: 3163292
dataset_size: 36883069
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
yao123/cloth_for_self333 | ---
license: other
---
|
cardiffnlp/tweet_topic_single | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: TweetTopicSingle
---
# Dataset Card for "cardiffnlp/tweet_topic_single"
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824)
- **Dataset:** Tweet Topic Dataset
- **Domain:** Twitter
- **Number of Class:** 6
### Dataset Summary
This is the official repository of TweetTopic (["Twitter Topic Classification
, COLING main conference 2022"](https://arxiv.org/abs/2209.09824)), a topic classification dataset on Twitter with 6 labels.
Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
See [cardiffnlp/tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) for multi label version of TweetTopic.
The tweet collection used in TweetTopic is same as what used in [TweetNER7](https://huggingface.co/datasets/tner/tweetner7).
The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.
### Preprocessing
We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
For verified usernames, we replace its display name (or account name) with symbols `{@}`.
For example, a tweet
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from @herbiehancock
via @bluenoterecords link below:
http://bluenote.lnk.to/AlbumOfTheWeek
```
is transformed into the following text.
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from {@herbiehancock@}
via {@bluenoterecords@} link below: {{URL}}
```
A simple function to format tweet follows below.
```python
import re
from urlextract import URLExtract
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
target_format = format_tweet(target)
print(target_format)
'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
```
### Data Splits
| split | number of texts | description |
|:------------------------|-----:|------:|
| test_2020 | 376 | test dataset from September 2019 to August 2020 |
| test_2021 | 1693 | test dataset from September 2020 to August 2021 |
| train_2020 | 2858 | training dataset from September 2019 to August 2020 |
| train_2021 | 1516 | training dataset from September 2020 to August 2021 |
| train_all | 4374 | combined training dataset of `train_2020` and `train_2021` |
| validation_2020 | 352 | validation dataset from September 2019 to August 2020 |
| validation_2021 | 189 | validation dataset from September 2020 to August 2021 |
| train_random | 2830 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
| validation_random | 354 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
| test_coling2022_random | 3399 | random split used in the COLING 2022 paper |
| train_coling2022_random | 3598 | random split used in the COLING 2022 paper |
| test_coling2022 | 3399 | temporal split used in the COLING 2022 paper |
| train_coling2022 | 3598 | temporal split used in the COLING 2022 paper |
For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `train_coling2022` and `test_coling2022` for temporal-shift, and `train_coling2022_random` and `test_coling2022_random` fir random split (the coling2022 split does not have validation set).
### Models
| model | training data | F1 | F1 (macro) | Accuracy |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|---------:|-------------:|-----------:|
| [cardiffnlp/roberta-large-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-all) | all (2020 + 2021) | 0.896043 | 0.800061 | 0.896043 |
| [cardiffnlp/roberta-base-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-all) | all (2020 + 2021) | 0.887773 | 0.79793 | 0.887773 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all) | all (2020 + 2021) | 0.892499 | 0.774494 | 0.892499 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all) | all (2020 + 2021) | 0.890136 | 0.776025 | 0.890136 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all) | all (2020 + 2021) | 0.894861 | 0.800952 | 0.894861 |
| [cardiffnlp/roberta-large-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-2020) | 2020 only | 0.878913 | 0.70565 | 0.878913 |
| [cardiffnlp/roberta-base-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-2020) | 2020 only | 0.868281 | 0.729667 | 0.868281 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020) | 2020 only | 0.882457 | 0.740187 | 0.882457 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020) | 2020 only | 0.87596 | 0.746275 | 0.87596 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020) | 2020 only | 0.877732 | 0.746119 | 0.877732 |
Model fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py).
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```python
{
"text": "Game day for {{USERNAME}} U18\u2019s against {{USERNAME}} U18\u2019s. Even though it\u2019s a \u2018home\u2019 game for the people that have settled in Mid Wales it\u2019s still a 4 hour round trip for us up to Colwyn Bay. Still enjoy it though!",
"date": "2019-09-08",
"label": 4,
"id": "1170606779568463874",
"label_name": "sports_&_gaming"
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_single/raw/main/dataset/label.single.json).
```python
{
"arts_&_culture": 0,
"business_&_entrepreneurs": 1,
"pop_culture": 2,
"daily_life": 3,
"sports_&_gaming": 4,
"science_&_technology": 5
}
```
### Citation Information
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
``` |
JoshRedmondUK/LatamSat | ---
license: cc-by-3.0
---
|
HuggingFaceM4/VizWiz-Sample | Invalid username or password. |
Asap7772/ultrafeedback_binarized_narrow | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: reward_chosen
dtype: float64
- name: reward_rejected
dtype: float64
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
splits:
- name: train_prefs
num_bytes: 184309550
num_examples: 60672
download_size: 109198612
dataset_size: 184309550
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
---
# Dataset Card for "ultrafeedback_binarized_narrow"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pixelpandacreative/ember_expanded_002 | ---
license: apache-2.0
task_categories:
- table-question-answering
language:
- en
size_categories:
- 10K<n<100K
--- |
LambdaTests/VQAv2_sample_validation_benchmarks_partition_5 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 55
num_examples: 2
download_size: 0
dataset_size: 55
---
# Dataset Card for "VQAv2_sample_validation_benchmarks_partition_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
freshpearYoon/v3_train_free_concat_22 | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 3842542048
num_examples: 2500
download_size: 1797940263
dataset_size: 3842542048
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-staging-eval-project-d42d3c12-7815007 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: evs/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: evs/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
eduardem/powpogy | ---
license: apache-2.0
---
# Powpogy Fine-Tuning Dataset
## License
This dataset is licensed under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
## Introduction
This dataset was created to address the need for assessing various fine-tuning methods for machine learning models. The ultimate goal is to use this dataset to fine-tune pre-trained models and evaluate their ability to retain knowledge.
## Objective
The primary objective is to offer a dataset with entirely new information that is not part of the training data for any existing models. By using this dataset, you can fine-tune a pre-trained model and assess the effectiveness of various fine-tuning techniques, particularly in terms of knowledge retention.
## About Powpogy
Powpogy is a fictional superhero who does not exist in the training data of any current base or fine-tuned models. This dataset contains diverse information about Powpogy, making it an ideal resource for fine-tuning experiments.
## Usage
This dataset can be used to:
- Fine-tune pre-trained models
- Validate the efficacy of different fine-tuning methods
- Test the degree of knowledge retention in fine-tuned models
## Contributing
If you have suggestions for improvements or additions to the dataset, feel free to open an issue or submit a pull request.
|
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