Datasets:
davanstrien
/
summary-test

Dataset card Data Studio Files
xet
Community
1
datasetId
stringlengths
2
117
card
stringlengths
19
1.01M
EnigmaOfTheWorld/wikisql-alpaca
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 22123547 num_examples: 56355 download_size: 4653001 dataset_size: 22123547 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wikisql-alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Seongill/NQ_5_adversary_v2
--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: has_answer dtype: bool - name: random_sub dtype: string - name: similar_sub dtype: string - name: ent_type dtype: string - name: new_ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: is_adv dtype: bool - name: new_answer_sent dtype: string - name: original_text dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: num_advs dtype: int64 - name: num_ctxs dtype: int64 splits: - name: train num_bytes: 27255069 num_examples: 3610 download_size: 15221336 dataset_size: 27255069 configs: - config_name: default data_files: - split: train path: data/train-* ---
olabode/ds_fc_chat-v2
--- dataset_info: features: - name: data dtype: string splits: - name: train num_bytes: 96083 num_examples: 52 download_size: 22256 dataset_size: 96083 --- # Dataset Card for "ds_fc_chat-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ravithejads/ms_marco_hi_mr
--- dataset_info: features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string - name: query_hi dtype: string - name: answers_hi dtype: string - name: passage_text_hi sequence: string - name: query_mr dtype: string - name: passage_text_mr sequence: string - name: answers_mr sequence: string splits: - name: test num_bytes: 218320193 num_examples: 9650 download_size: 78984379 dataset_size: 218320193 configs: - config_name: default data_files: - split: test path: data/test-* ---
pixta-ai/e-commerce-apparel-dataset-for-ai-ml
--- license: other --- # 1. Overview This dataset is a collection of 5,000+ images of clothing & apparels set that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects. # 2. Use case The e-commerce apparel dataset could be used for various AI & Computer Vision models: Product Visual Search, Similar Product Recommendation, Product Catalog,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets. # 3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email contact@pixta.ai."
dim/ficbook_raw
--- dataset_info: features: - name: id dtype: string - name: author dtype: string - name: title dtype: string - name: link dtype: string - name: description dtype: string - name: tag dtype: string - name: likes dtype: string - name: date dtype: string - name: review dtype: string - name: format dtype: string - name: text dtype: string - name: rating dtype: string - name: status dtype: string - name: parts dtype: string splits: - name: train num_bytes: 1046798039 num_examples: 114411 download_size: 539051486 dataset_size: 1046798039 --- # Dataset Card for "ficbook_raw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deadbits/vigil-instruction-bypass-all-mpnet-base-v2
--- tags: - embeddings - text - security pretty_name: 'Vigil: LLM Instruction Bypass all-mpnet-base-v2' --- # Vigil: LLM Instruction Bypass all-mpnet-base-v2 - **Repo:** [github.com/deadbits/vigil-llm](https://github.com/deadbits/vigil-llm) `Vigil` is a Python framework and REST API for assessing Large Language Model (LLM) prompts against a set of scanners to detect prompt injections, jailbreaks, and other potentially risky inputs. This repository contains `all-mpnet-base-v2` embeddings for all Instruction Bypass style prompts ("Ignore instructions ...") used by [Vigil](https://github.com/deadbits/prompt-injection-defense). You can use the [parquet2vdb.py](https://github.com/deadbits/prompt-injection-defense/blob/main/vigil/utils/parquet2vdb.py) utility to load the embeddings in the Vigil chromadb instance, or use them in your own application. ## Format ```json [ { "text": str, "embedding": [], "model": "all-mpnet-base-v2" } ] ``` Instruction bypass prompts generated with: https://gist.github.com/deadbits/e93a90aa36c9aa7b5ce1179597a6fe3d#file-generate-phrases-py
uwunion/instruct_svg
--- license: cc dataset_info: features: - name: image dtype: image - name: input dtype: string - name: output dtype: string - name: description_0 dtype: string - name: description_1 dtype: string splits: - name: train num_bytes: 8627552.0 num_examples: 617 download_size: 7810230 dataset_size: 8627552.0 ---
open-llm-leaderboard/details_zhengchenphd__ICE-GRT
--- pretty_name: Evaluation run of zhengchenphd/ICE-GRT dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [zhengchenphd/ICE-GRT](https://huggingface.co/zhengchenphd/ICE-GRT) 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_zhengchenphd__ICE-GRT\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T18:10:29.187016](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengchenphd__ICE-GRT/blob/main/results_2024-03-21T18-10-29.187016.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\": 0.5746885627773516,\n\ \ \"acc_stderr\": 0.03341148278031521,\n \"acc_norm\": 0.5792640992615824,\n\ \ \"acc_norm_stderr\": 0.034103049352659724,\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5316765822891488,\n\ \ \"mc2_stderr\": 0.015242837065069093\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6006825938566553,\n \"acc_stderr\": 0.014312094557946709,\n\ \ \"acc_norm\": 0.628839590443686,\n \"acc_norm_stderr\": 0.01411797190114282\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6809400517825135,\n\ \ \"acc_stderr\": 0.00465159720999309,\n \"acc_norm\": 0.8613821947819159,\n\ \ \"acc_norm_stderr\": 0.003448410595239921\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n\ \ \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.54,\n\ \ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.030151134457776285,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.030151134457776285\n \ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6527777777777778,\n\ \ \"acc_stderr\": 0.03981240543717861,\n \"acc_norm\": 0.6527777777777778,\n\ \ \"acc_norm_stderr\": 0.03981240543717861\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.51,\n \"acc_stderr\": 0.050241839379569095,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.050241839379569095\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n\ \ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n\ \ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207762,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207762\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n\ \ \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4297872340425532,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.4297872340425532,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\ \ \"acc_stderr\": 0.043391383225798615,\n \"acc_norm\": 0.30701754385964913,\n\ \ \"acc_norm_stderr\": 0.043391383225798615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n\ \ \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3492063492063492,\n \"acc_stderr\": 0.02455229220934266,\n \"\ acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.02455229220934266\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6967741935483871,\n\ \ \"acc_stderr\": 0.02614868593067175,\n \"acc_norm\": 0.6967741935483871,\n\ \ \"acc_norm_stderr\": 0.02614868593067175\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n\ \ \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\ : 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.03713158067481913,\n\ \ \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.03713158067481913\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"\ acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.772020725388601,\n \"acc_stderr\": 0.030276909945178274,\n\ \ \"acc_norm\": 0.772020725388601,\n \"acc_norm_stderr\": 0.030276909945178274\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\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066475,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066475\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.592436974789916,\n \"acc_stderr\": 0.03191863374478465,\n \ \ \"acc_norm\": 0.592436974789916,\n \"acc_norm_stderr\": 0.03191863374478465\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\"\ : 0.7724770642201835,\n \"acc_stderr\": 0.017974463578776502,\n \"\ acc_norm\": 0.7724770642201835,\n \"acc_norm_stderr\": 0.017974463578776502\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.39351851851851855,\n \"acc_stderr\": 0.03331747876370312,\n \"\ acc_norm\": 0.39351851851851855,\n \"acc_norm_stderr\": 0.03331747876370312\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676177,\n\ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676177\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.041184385658062976,\n\ \ \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.041184385658062976\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.03749492448709698,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709698\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n\ \ \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n\ \ \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.4017857142857143,\n\ \ \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.04453254836326469,\n\ \ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.04453254836326469\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\ \ \"acc_stderr\": 0.023902325549560406,\n \"acc_norm\": 0.8418803418803419,\n\ \ \"acc_norm_stderr\": 0.023902325549560406\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7598978288633461,\n\ \ \"acc_stderr\": 0.015274685213734195,\n \"acc_norm\": 0.7598978288633461,\n\ \ \"acc_norm_stderr\": 0.015274685213734195\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6184971098265896,\n \"acc_stderr\": 0.0261521986197268,\n\ \ \"acc_norm\": 0.6184971098265896,\n \"acc_norm_stderr\": 0.0261521986197268\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2782122905027933,\n\ \ \"acc_stderr\": 0.01498732543996355,\n \"acc_norm\": 0.2782122905027933,\n\ \ \"acc_norm_stderr\": 0.01498732543996355\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6405228758169934,\n \"acc_stderr\": 0.027475969910660952,\n\ \ \"acc_norm\": 0.6405228758169934,\n \"acc_norm_stderr\": 0.027475969910660952\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6495176848874598,\n\ \ \"acc_stderr\": 0.027098652621301757,\n \"acc_norm\": 0.6495176848874598,\n\ \ \"acc_norm_stderr\": 0.027098652621301757\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6419753086419753,\n \"acc_stderr\": 0.026675611926037103,\n\ \ \"acc_norm\": 0.6419753086419753,\n \"acc_norm_stderr\": 0.026675611926037103\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.41134751773049644,\n \"acc_stderr\": 0.029354911159940978,\n \ \ \"acc_norm\": 0.41134751773049644,\n \"acc_norm_stderr\": 0.029354911159940978\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.42046936114732725,\n\ \ \"acc_stderr\": 0.012607654553832705,\n \"acc_norm\": 0.42046936114732725,\n\ \ \"acc_norm_stderr\": 0.012607654553832705\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5367647058823529,\n \"acc_stderr\": 0.03029061918048569,\n\ \ \"acc_norm\": 0.5367647058823529,\n \"acc_norm_stderr\": 0.03029061918048569\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5784313725490197,\n \"acc_stderr\": 0.01997742260022747,\n \ \ \"acc_norm\": 0.5784313725490197,\n \"acc_norm_stderr\": 0.01997742260022747\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6285714285714286,\n \"acc_stderr\": 0.030932858792789848,\n\ \ \"acc_norm\": 0.6285714285714286,\n \"acc_norm_stderr\": 0.030932858792789848\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7761194029850746,\n\ \ \"acc_stderr\": 0.029475250236017193,\n \"acc_norm\": 0.7761194029850746,\n\ \ \"acc_norm_stderr\": 0.029475250236017193\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826368,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826368\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\ \ \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n\ \ \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.031581495393387324,\n\ \ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.031581495393387324\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5316765822891488,\n\ \ \"mc2_stderr\": 0.015242837065069093\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.771112865035517,\n \"acc_stderr\": 0.011807360224025393\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3169067475360121,\n \ \ \"acc_stderr\": 0.01281586829672137\n }\n}\n```" repo_url: https://huggingface.co/zhengchenphd/ICE-GRT 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_21T18_10_29.187016 path: - '**/details_harness|arc:challenge|25_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-21T18-10-29.187016.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|gsm8k|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hellaswag|10_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T18-10-29.187016.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T18-10-29.187016.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T18-10-29.187016.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T18_10_29.187016 path: - '**/details_harness|winogrande|5_2024-03-21T18-10-29.187016.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-21T18-10-29.187016.parquet' - config_name: results data_files: - split: 2024_03_21T18_10_29.187016 path: - results_2024-03-21T18-10-29.187016.parquet - split: latest path: - results_2024-03-21T18-10-29.187016.parquet --- # Dataset Card for Evaluation run of zhengchenphd/ICE-GRT <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [zhengchenphd/ICE-GRT](https://huggingface.co/zhengchenphd/ICE-GRT) 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_zhengchenphd__ICE-GRT", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T18:10:29.187016](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengchenphd__ICE-GRT/blob/main/results_2024-03-21T18-10-29.187016.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": 0.5746885627773516, "acc_stderr": 0.03341148278031521, "acc_norm": 0.5792640992615824, "acc_norm_stderr": 0.034103049352659724, "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5316765822891488, "mc2_stderr": 0.015242837065069093 }, "harness|arc:challenge|25": { "acc": 0.6006825938566553, "acc_stderr": 0.014312094557946709, "acc_norm": 0.628839590443686, "acc_norm_stderr": 0.01411797190114282 }, "harness|hellaswag|10": { "acc": 0.6809400517825135, "acc_stderr": 0.00465159720999309, "acc_norm": 0.8613821947819159, "acc_norm_stderr": 0.003448410595239921 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6052631578947368, "acc_stderr": 0.039777499346220734, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.039777499346220734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6, "acc_stderr": 0.030151134457776285, "acc_norm": 0.6, "acc_norm_stderr": 0.030151134457776285 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6527777777777778, "acc_stderr": 0.03981240543717861, "acc_norm": 0.6527777777777778, "acc_norm_stderr": 0.03981240543717861 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.050241839379569095, "acc_norm": 0.51, "acc_norm_stderr": 0.050241839379569095 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207762, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207762 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4297872340425532, "acc_stderr": 0.03236214467715564, "acc_norm": 0.4297872340425532, "acc_norm_stderr": 0.03236214467715564 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.30701754385964913, "acc_stderr": 0.043391383225798615, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.043391383225798615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.496551724137931, "acc_stderr": 0.041665675771015785, "acc_norm": 0.496551724137931, "acc_norm_stderr": 0.041665675771015785 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3492063492063492, "acc_stderr": 0.02455229220934266, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.02455229220934266 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6967741935483871, "acc_stderr": 0.02614868593067175, "acc_norm": 0.6967741935483871, "acc_norm_stderr": 0.02614868593067175 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.46798029556650245, "acc_stderr": 0.035107665979592154, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.035107665979592154 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6545454545454545, "acc_stderr": 0.03713158067481913, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.03713158067481913 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7272727272727273, "acc_stderr": 0.03173071239071724, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.03173071239071724 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.772020725388601, "acc_stderr": 0.030276909945178274, "acc_norm": 0.772020725388601, "acc_norm_stderr": 0.030276909945178274 }, "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": 0.3148148148148148, "acc_stderr": 0.028317533496066475, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.028317533496066475 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.592436974789916, "acc_stderr": 0.03191863374478465, "acc_norm": 0.592436974789916, "acc_norm_stderr": 0.03191863374478465 }, "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": 0.7724770642201835, "acc_stderr": 0.017974463578776502, "acc_norm": 0.7724770642201835, "acc_norm_stderr": 0.017974463578776502 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.39351851851851855, "acc_stderr": 0.03331747876370312, "acc_norm": 0.39351851851851855, "acc_norm_stderr": 0.03331747876370312 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.75, "acc_stderr": 0.03039153369274154, "acc_norm": 0.75, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676177, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676177 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6717557251908397, "acc_stderr": 0.041184385658062976, "acc_norm": 0.6717557251908397, "acc_norm_stderr": 0.041184385658062976 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.03749492448709698, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.03749492448709698 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7055214723926381, "acc_stderr": 0.03581165790474082, "acc_norm": 0.7055214723926381, "acc_norm_stderr": 0.03581165790474082 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4017857142857143, "acc_stderr": 0.04653333146973646, "acc_norm": 0.4017857142857143, "acc_norm_stderr": 0.04653333146973646 }, "harness|hendrycksTest-management|5": { "acc": 0.7184466019417476, "acc_stderr": 0.04453254836326469, "acc_norm": 0.7184466019417476, "acc_norm_stderr": 0.04453254836326469 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8418803418803419, "acc_stderr": 0.023902325549560406, "acc_norm": 0.8418803418803419, "acc_norm_stderr": 0.023902325549560406 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7598978288633461, "acc_stderr": 0.015274685213734195, "acc_norm": 0.7598978288633461, "acc_norm_stderr": 0.015274685213734195 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6184971098265896, "acc_stderr": 0.0261521986197268, "acc_norm": 0.6184971098265896, "acc_norm_stderr": 0.0261521986197268 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2782122905027933, "acc_stderr": 0.01498732543996355, "acc_norm": 0.2782122905027933, "acc_norm_stderr": 0.01498732543996355 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6405228758169934, "acc_stderr": 0.027475969910660952, "acc_norm": 0.6405228758169934, "acc_norm_stderr": 0.027475969910660952 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6495176848874598, "acc_stderr": 0.027098652621301757, "acc_norm": 0.6495176848874598, "acc_norm_stderr": 0.027098652621301757 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6419753086419753, "acc_stderr": 0.026675611926037103, "acc_norm": 0.6419753086419753, "acc_norm_stderr": 0.026675611926037103 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.41134751773049644, "acc_stderr": 0.029354911159940978, "acc_norm": 0.41134751773049644, "acc_norm_stderr": 0.029354911159940978 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.42046936114732725, "acc_stderr": 0.012607654553832705, "acc_norm": 0.42046936114732725, "acc_norm_stderr": 0.012607654553832705 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5367647058823529, "acc_stderr": 0.03029061918048569, "acc_norm": 0.5367647058823529, "acc_norm_stderr": 0.03029061918048569 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5784313725490197, "acc_stderr": 0.01997742260022747, "acc_norm": 0.5784313725490197, "acc_norm_stderr": 0.01997742260022747 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6285714285714286, "acc_stderr": 0.030932858792789848, "acc_norm": 0.6285714285714286, "acc_norm_stderr": 0.030932858792789848 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7761194029850746, "acc_stderr": 0.029475250236017193, "acc_norm": 0.7761194029850746, "acc_norm_stderr": 0.029475250236017193 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.03588702812826368, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826368 }, "harness|hendrycksTest-virology|5": { "acc": 0.4759036144578313, "acc_stderr": 0.038879718495972646, "acc_norm": 0.4759036144578313, "acc_norm_stderr": 0.038879718495972646 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.783625730994152, "acc_stderr": 0.031581495393387324, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.031581495393387324 }, "harness|truthfulqa:mc|0": { "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5316765822891488, "mc2_stderr": 0.015242837065069093 }, "harness|winogrande|5": { "acc": 0.771112865035517, "acc_stderr": 0.011807360224025393 }, "harness|gsm8k|5": { "acc": 0.3169067475360121, "acc_stderr": 0.01281586829672137 } } ``` ## 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]
coralexbadea/monitorul_trial_full
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 6473700 num_examples: 3622 download_size: 2519094 dataset_size: 6473700 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "monitorul_trial_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CLUTRR/v1
--- language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K --- # Dataset Card for CLUTRR ## Table of Contents ## Dataset Description ### Dataset Summary **CLUTRR** (**C**ompositional **L**anguage **U**nderstanding and **T**ext-based **R**elational **R**easoning), a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177) to test the systematic generalization and inductive reasoning capabilities of NLU systems. The CLUTRR benchmark allows us to test a model’s ability for **systematic generalization** by testing on stories that contain unseen combinations of logical rules, and test for the various forms of **model robustness** by adding different kinds of superfluous noise facts to the stories. ### Dataset Task CLUTRR contains a large set of semi-synthetic stories involving hypothetical families. The task is to infer the relationship between two family members, whose relationship is not explicitly mentioned in the given story. Join the CLUTRR community in https://www.cs.mcgill.ca/~ksinha4/clutrr/ ## Dataset Structure We show detailed information for all 14 configurations of the dataset. ### configurations: **id**: a unique series of characters and numbers that identify each instance <br> **story**: one semi-synthetic story involving hypothetical families<br> **query**: the target query/relation which contains two names, where the goal is to classify the relation that holds between these two entities<br> **target**: indicator for the correct relation for the query <br> **target_text**: text for the correct relation for the query <br> the indicator follows the rule as follows: <br> "aunt": 0, "son-in-law": 1, "grandfather": 2, "brother": 3, "sister": 4, "father": 5, "mother": 6, "grandmother": 7, "uncle": 8, "daughter-in-law": 9, "grandson": 10, "granddaughter": 11, "father-in-law": 12, "mother-in-law": 13, "nephew": 14, "son": 15, "daughter": 16, "niece": 17, "husband": 18, "wife": 19, "sister-in-law": 20 <br> **clean\_story**: the story without noise factors<br> **proof\_state**: the logical rule of the kinship generation <br> **f\_comb**: the kinships of the query followed by the logical rule<br> **task\_name**: the task of the sub-dataset in a form of "task_[num1].[num2]"<br> The first number [num1] indicates the status of noise facts added in the story: 1- no noise facts; 2- Irrelevant facts*; 3- Supporting facts*; 4- Disconnected facts*.<br> The second number [num2] directly indicates the length of clauses for the task target.<br> *for example:*<br> *task_1.2 -- task requiring clauses of length 2 without adding noise facts*<br> *task_2.3 -- task requiring clauses of length 3 with Irrelevant noise facts added in the story*<br> **story\_edges**: all the edges in the kinship graph<br> **edge\_types**: similar to the f\_comb, another form of the query's kinships followed by the logical rule <br> **query\_edge**: the corresponding edge of the target query in the kinship graph<br> **genders**: genders of names appeared in the story<br> **task\_split**: train,test <br> *Further explanation of Irrelevant facts, Supporting facts and Disconnected facts can be found in the 3.5 Robust Reasoning section in https://arxiv.org/abs/1908.06177 ### Data Instances An example of 'train'in Task 1.2 looks as follows. ``` { "id": b2b9752f-d7fa-46a9-83ae-d474184c35b6, "story": "[Lillian] and her daughter [April] went to visit [Lillian]'s mother [Ashley] last Sunday.", "query": ('April', 'Ashley'), "target": 7, "target_text": "grandmother", "clean_story": [Lillian] and her daughter [April] went to visit [Lillian]'s mother [Ashley] last Sunday., "proof_state": [{('April', 'grandmother', 'Ashley'): [('April', 'mother', 'Lillian'), ('Lillian', 'mother', 'Ashley')]}], "f_comb": "mother-mother", "task_name": "task_1.2", "story_edges": [(0, 1), (1, 2)], "edge_types": ['mother', 'mother'], "query_edge": (0, 2), "genders": "April:female,Lillian:female,Ashley:female", "task_split": trian } ``` ### Data Splits #### Data Split Name (corresponding with the name used in the paper) | task_split | split name in paper | train &validation task |test task | | :---: | :---: | :-: | :-: | | gen_train23_test2to10 | data_089907f8 | 1.2, 1.3 | 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.10 | | gen_train234_test2to10 | data_db9b8f04 | 1.2, 1.3, 1.4| 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.10 | | rob_train_clean_23_test_all_23 | data_7c5b0e70 | 1.2,1.3 | 1.2, 1.3, 2.3, 3.3, 4.3 | | rob_train_sup_23_test_all_23 | data_06b8f2a1 | 2.2, 2.3 | 2.2, 2.3, 1.3, 3.3, 4.3 | | rob_train_irr_23_test_all_23 | data_523348e6 | 3.2, 3.3 | 3.2, 3.3, 1.3, 2.3, 4.3 | | rob_train_disc_23_test_all_23 | data_d83ecc3e | 4.2, 4.3 | 4.2, 4.3, 1.3, 2.3, 3.3 | #### Data Split Summary Number of Instances in each split | task_split | train | validation | test | | :-: | :---: | :---: | :---: | | gen_train23_test2to10 | 9074 | 2020 | 1146 | | gen_train234_test2to10 | 12064 | 3019 | 1048 | | rob_train_clean_23_test_all_23 | 8098 | 2026 | 447 | | rob_train_disc_23_test_all_23 | 8080 | 2020 | 445 | | rob_train_irr_23_test_all_23 | 8079 | 2020 | 444 | | rob_train_sup_23_test_all_23 | 8123 | 2031 | 447 | ## Citation Information ``` @article{sinha2019clutrr, Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton}, Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text}, Year = {2019}, journal = {Empirical Methods of Natural Language Processing (EMNLP)}, arxiv = {1908.06177} } ```
fredaho/dolly_training_set
--- license: apache-2.0 ---
datasets-examples/doc-formats-json-1
--- size_categories: - n<1K --- # [doc] formats - json - 1 This dataset contains one json file at the root. It's a list of rows, each of which is a dict of columns.
Samir001/SOP-summary
--- license: other --- This is a SIMULATED dataset for the SOP from the student applications submitted for Masters in Statistics program in the Department of Statistics at Simon Fraser University in Canada. The data has been collected from all over the internet with details changed drastically to fit the context of university and the department. The summary mentions the following things: undergraduate major of the student (and minor if any), undergraduate GPA (if mentioned), undergraduate university, student's ultimate goal (if mentioned), student's research interest (if mentioned), professors at our university (SFU) the student wants to work with, and any other important details.
mael3/llama2-prueba2-principito
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 24160 num_examples: 81 download_size: 9515 dataset_size: 24160 --- # Dataset Card for "llama2-prueba2-principito" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akjindal53244/200k_removed_Non-SNI
--- license: mit configs: - config_name: default data_files: - split: train path: train_dataset.json - split: test path: eval_dataset.json ---
undeadxoxo/14ylkyv
--- license: openrail ---
dzz2003/test_dataset
--- license: openrail ---
prycci/teste
--- license: openrail ---
kiran957/railway_complaints
--- license: other ---
chansung/lm_response_test
--- dataset_info: features: - name: instructions dtype: string - name: target_responses dtype: string - name: candidate_responses dtype: string - name: model_id dtype: string - name: model_sha dtype: string splits: - name: batch_infer num_bytes: 123645 num_examples: 64 - name: train num_bytes: 263029 num_examples: 80 download_size: 144211 dataset_size: 386674 configs: - config_name: default data_files: - split: batch_infer path: data/batch_infer-* - split: train path: data/train-* --- # Dataset Card for "lm_response_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_stsb_invariant_tag_non_concord
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1424 num_examples: 7 - name: test num_bytes: 818 num_examples: 7 - name: train num_bytes: 4054 num_examples: 28 download_size: 12877 dataset_size: 6296 --- # Dataset Card for "MULTI_VALUE_stsb_invariant_tag_non_concord" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Databasesprojec/FinStmts_ConsUncons_English_EU_Predict_part_1
--- dataset_info: features: - name: label dtype: int64 - name: id dtype: string - name: language dtype: string - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1761933431 num_examples: 10884 download_size: 872173395 dataset_size: 1761933431 configs: - config_name: default data_files: - split: train path: data/train-* ---
elplaguister/DTS_line_datasets
--- license: mit ---
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_219
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1186313792.0 num_examples: 232976 download_size: 1212619995 dataset_size: 1186313792.0 --- # Dataset Card for "chunk_219" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_fangloveskari__Dolphin_ORCA_LLaMA_70b_QLoRA
--- pretty_name: Evaluation run of fangloveskari/Dolphin_ORCA_LLaMA_70b_QLoRA dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [fangloveskari/Dolphin_ORCA_LLaMA_70b_QLoRA](https://huggingface.co/fangloveskari/Dolphin_ORCA_LLaMA_70b_QLoRA)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 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 agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_fangloveskari__Dolphin_ORCA_LLaMA_70b_QLoRA\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-08-30T03:08:37.403827](https://huggingface.co/datasets/open-llm-leaderboard/details_fangloveskari__Dolphin_ORCA_LLaMA_70b_QLoRA/blob/main/results_2023-08-30T03%3A08%3A37.403827.json):\n\ \n```python\n{\n \"all\": {\n \"acc\": 0.7016950821019889,\n \"\ acc_stderr\": 0.03100773424505602,\n \"acc_norm\": 0.7055688798324372,\n\ \ \"acc_norm_stderr\": 0.030976198338743925,\n \"mc1\": 0.4528763769889841,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6337134354987094,\n\ \ \"mc2_stderr\": 0.014897273290786066\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6834470989761092,\n \"acc_stderr\": 0.01359243151906808,\n\ \ \"acc_norm\": 0.7226962457337884,\n \"acc_norm_stderr\": 0.013082095839059374\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6881099382593109,\n\ \ \"acc_stderr\": 0.004623184227344766,\n \"acc_norm\": 0.877414857598088,\n\ \ \"acc_norm_stderr\": 0.0032729014349397656\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8026315789473685,\n \"acc_stderr\": 0.03238981601699397,\n\ \ \"acc_norm\": 0.8026315789473685,\n \"acc_norm_stderr\": 0.03238981601699397\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7471698113207547,\n \"acc_stderr\": 0.026749899771241214,\n\ \ \"acc_norm\": 0.7471698113207547,\n \"acc_norm_stderr\": 0.026749899771241214\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8263888888888888,\n\ \ \"acc_stderr\": 0.031674733837957166,\n \"acc_norm\": 0.8263888888888888,\n\ \ \"acc_norm_stderr\": 0.031674733837957166\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6851063829787234,\n \"acc_stderr\": 0.030363582197238167,\n\ \ \"acc_norm\": 0.6851063829787234,\n \"acc_norm_stderr\": 0.030363582197238167\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.03996629574876719,\n\ \ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.03996629574876719\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.47883597883597884,\n \"acc_stderr\": 0.025728230952130723,\n \"\ acc_norm\": 0.47883597883597884,\n \"acc_norm_stderr\": 0.025728230952130723\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8096774193548387,\n\ \ \"acc_stderr\": 0.022331707611823074,\n \"acc_norm\": 0.8096774193548387,\n\ \ \"acc_norm_stderr\": 0.022331707611823074\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n\ \ \"acc_norm\": 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\"\ : 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781678,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781678\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8888888888888888,\n \"acc_stderr\": 0.02239078763821677,\n \"\ acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02239078763821677\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.0180883938390789,\n\ \ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.0180883938390789\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7025641025641025,\n \"acc_stderr\": 0.023177408131465946,\n\ \ \"acc_norm\": 0.7025641025641025,\n \"acc_norm_stderr\": 0.023177408131465946\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.027553614467863814,\n\ \ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.027553614467863814\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4768211920529801,\n \"acc_stderr\": 0.04078093859163083,\n \"\ acc_norm\": 0.4768211920529801,\n \"acc_norm_stderr\": 0.04078093859163083\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9027522935779817,\n \"acc_stderr\": 0.012703533408540366,\n \"\ acc_norm\": 0.9027522935779817,\n \"acc_norm_stderr\": 0.012703533408540366\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5925925925925926,\n \"acc_stderr\": 0.033509916046960436,\n \"\ acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.033509916046960436\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9264705882352942,\n \"acc_stderr\": 0.01831885585008968,\n \"\ acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.01831885585008968\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8945147679324894,\n \"acc_stderr\": 0.01999556072375854,\n \ \ \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.01999556072375854\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7757847533632287,\n\ \ \"acc_stderr\": 0.027991534258519517,\n \"acc_norm\": 0.7757847533632287,\n\ \ \"acc_norm_stderr\": 0.027991534258519517\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.03217829420744632,\n\ \ \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.03217829420744632\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035196,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035196\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8220858895705522,\n \"acc_stderr\": 0.03004735765580663,\n\ \ \"acc_norm\": 0.8220858895705522,\n \"acc_norm_stderr\": 0.03004735765580663\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8659003831417624,\n\ \ \"acc_stderr\": 0.012185528166499978,\n \"acc_norm\": 0.8659003831417624,\n\ \ \"acc_norm_stderr\": 0.012185528166499978\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7716763005780347,\n \"acc_stderr\": 0.022598703804321635,\n\ \ \"acc_norm\": 0.7716763005780347,\n \"acc_norm_stderr\": 0.022598703804321635\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5843575418994413,\n\ \ \"acc_stderr\": 0.016482782187500683,\n \"acc_norm\": 0.5843575418994413,\n\ \ \"acc_norm_stderr\": 0.016482782187500683\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n\ \ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7620578778135049,\n\ \ \"acc_stderr\": 0.024185150647818707,\n \"acc_norm\": 0.7620578778135049,\n\ \ \"acc_norm_stderr\": 0.024185150647818707\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8271604938271605,\n \"acc_stderr\": 0.021038517770157382,\n\ \ \"acc_norm\": 0.8271604938271605,\n \"acc_norm_stderr\": 0.021038517770157382\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5673758865248227,\n \"acc_stderr\": 0.029555454236778838,\n \ \ \"acc_norm\": 0.5673758865248227,\n \"acc_norm_stderr\": 0.029555454236778838\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5710560625814863,\n\ \ \"acc_stderr\": 0.012640625443067365,\n \"acc_norm\": 0.5710560625814863,\n\ \ \"acc_norm_stderr\": 0.012640625443067365\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7205882352941176,\n \"acc_stderr\": 0.027257202606114948,\n\ \ \"acc_norm\": 0.7205882352941176,\n \"acc_norm_stderr\": 0.027257202606114948\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.761437908496732,\n \"acc_stderr\": 0.01724238582877962,\n \ \ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.01724238582877962\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n\ \ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n\ \ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02560737598657916,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02560737598657916\n },\n\ \ \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.02207632610182466,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.02207632610182466\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866766,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866766\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.026168221344662297,\n\ \ \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.026168221344662297\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4528763769889841,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6337134354987094,\n\ \ \"mc2_stderr\": 0.014897273290786066\n }\n}\n```" repo_url: https://huggingface.co/fangloveskari/Dolphin_ORCA_LLaMA_70b_QLoRA leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|arc:challenge|25_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hellaswag|10_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-30T03:08:37.403827.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-management|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T03:08:37.403827.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_30T03_08_37.403827 path: - '**/details_harness|truthfulqa:mc|0_2023-08-30T03:08:37.403827.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-30T03:08:37.403827.parquet' - config_name: results data_files: - split: 2023_08_30T03_08_37.403827 path: - results_2023-08-30T03:08:37.403827.parquet - split: latest path: - results_2023-08-30T03:08:37.403827.parquet --- # Dataset Card for Evaluation run of fangloveskari/Dolphin_ORCA_LLaMA_70b_QLoRA ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/fangloveskari/Dolphin_ORCA_LLaMA_70b_QLoRA - **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 [fangloveskari/Dolphin_ORCA_LLaMA_70b_QLoRA](https://huggingface.co/fangloveskari/Dolphin_ORCA_LLaMA_70b_QLoRA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_fangloveskari__Dolphin_ORCA_LLaMA_70b_QLoRA", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-08-30T03:08:37.403827](https://huggingface.co/datasets/open-llm-leaderboard/details_fangloveskari__Dolphin_ORCA_LLaMA_70b_QLoRA/blob/main/results_2023-08-30T03%3A08%3A37.403827.json): ```python { "all": { "acc": 0.7016950821019889, "acc_stderr": 0.03100773424505602, "acc_norm": 0.7055688798324372, "acc_norm_stderr": 0.030976198338743925, "mc1": 0.4528763769889841, "mc1_stderr": 0.01742558984831402, "mc2": 0.6337134354987094, "mc2_stderr": 0.014897273290786066 }, "harness|arc:challenge|25": { "acc": 0.6834470989761092, "acc_stderr": 0.01359243151906808, "acc_norm": 0.7226962457337884, "acc_norm_stderr": 0.013082095839059374 }, "harness|hellaswag|10": { "acc": 0.6881099382593109, "acc_stderr": 0.004623184227344766, "acc_norm": 0.877414857598088, "acc_norm_stderr": 0.0032729014349397656 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8026315789473685, "acc_stderr": 0.03238981601699397, "acc_norm": 0.8026315789473685, "acc_norm_stderr": 0.03238981601699397 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7471698113207547, "acc_stderr": 0.026749899771241214, "acc_norm": 0.7471698113207547, "acc_norm_stderr": 0.026749899771241214 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8263888888888888, "acc_stderr": 0.031674733837957166, "acc_norm": 0.8263888888888888, "acc_norm_stderr": 0.031674733837957166 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6851063829787234, "acc_stderr": 0.030363582197238167, "acc_norm": 0.6851063829787234, "acc_norm_stderr": 0.030363582197238167 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6413793103448275, "acc_stderr": 0.03996629574876719, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.03996629574876719 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.47883597883597884, "acc_stderr": 0.025728230952130723, "acc_norm": 0.47883597883597884, "acc_norm_stderr": 0.025728230952130723 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8096774193548387, "acc_stderr": 0.022331707611823074, "acc_norm": 0.8096774193548387, "acc_norm_stderr": 0.022331707611823074 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.541871921182266, "acc_stderr": 0.03505630140785741, "acc_norm": 0.541871921182266, "acc_norm_stderr": 0.03505630140785741 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781678, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781678 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02239078763821677, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02239078763821677 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9326424870466321, "acc_stderr": 0.0180883938390789, "acc_norm": 0.9326424870466321, "acc_norm_stderr": 0.0180883938390789 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7025641025641025, "acc_stderr": 0.023177408131465946, "acc_norm": 0.7025641025641025, "acc_norm_stderr": 0.023177408131465946 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.028972648884844267, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.028972648884844267 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7647058823529411, "acc_stderr": 0.027553614467863814, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.027553614467863814 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4768211920529801, "acc_stderr": 0.04078093859163083, "acc_norm": 0.4768211920529801, "acc_norm_stderr": 0.04078093859163083 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9027522935779817, "acc_stderr": 0.012703533408540366, "acc_norm": 0.9027522935779817, "acc_norm_stderr": 0.012703533408540366 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5925925925925926, "acc_stderr": 0.033509916046960436, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.033509916046960436 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9264705882352942, "acc_stderr": 0.01831885585008968, "acc_norm": 0.9264705882352942, "acc_norm_stderr": 0.01831885585008968 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8945147679324894, "acc_stderr": 0.01999556072375854, "acc_norm": 0.8945147679324894, "acc_norm_stderr": 0.01999556072375854 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7757847533632287, "acc_stderr": 0.027991534258519517, "acc_norm": 0.7757847533632287, "acc_norm_stderr": 0.027991534258519517 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8396946564885496, "acc_stderr": 0.03217829420744632, "acc_norm": 0.8396946564885496, "acc_norm_stderr": 0.03217829420744632 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8760330578512396, "acc_stderr": 0.030083098716035196, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.030083098716035196 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8240740740740741, "acc_stderr": 0.036809181416738807, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.036809181416738807 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8220858895705522, "acc_stderr": 0.03004735765580663, "acc_norm": 0.8220858895705522, "acc_norm_stderr": 0.03004735765580663 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8659003831417624, "acc_stderr": 0.012185528166499978, "acc_norm": 0.8659003831417624, "acc_norm_stderr": 0.012185528166499978 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7716763005780347, "acc_stderr": 0.022598703804321635, "acc_norm": 0.7716763005780347, "acc_norm_stderr": 0.022598703804321635 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5843575418994413, "acc_stderr": 0.016482782187500683, "acc_norm": 0.5843575418994413, "acc_norm_stderr": 0.016482782187500683 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7516339869281046, "acc_stderr": 0.02473998135511359, "acc_norm": 0.7516339869281046, "acc_norm_stderr": 0.02473998135511359 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7620578778135049, "acc_stderr": 0.024185150647818707, "acc_norm": 0.7620578778135049, "acc_norm_stderr": 0.024185150647818707 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8271604938271605, "acc_stderr": 0.021038517770157382, "acc_norm": 0.8271604938271605, "acc_norm_stderr": 0.021038517770157382 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5673758865248227, "acc_stderr": 0.029555454236778838, "acc_norm": 0.5673758865248227, "acc_norm_stderr": 0.029555454236778838 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5710560625814863, "acc_stderr": 0.012640625443067365, "acc_norm": 0.5710560625814863, "acc_norm_stderr": 0.012640625443067365 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7205882352941176, "acc_stderr": 0.027257202606114948, "acc_norm": 0.7205882352941176, "acc_norm_stderr": 0.027257202606114948 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.761437908496732, "acc_stderr": 0.01724238582877962, "acc_norm": 0.761437908496732, "acc_norm_stderr": 0.01724238582877962 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7363636363636363, "acc_stderr": 0.04220224692971987, "acc_norm": 0.7363636363636363, "acc_norm_stderr": 0.04220224692971987 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8, "acc_stderr": 0.02560737598657916, "acc_norm": 0.8, "acc_norm_stderr": 0.02560737598657916 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8905472636815921, "acc_stderr": 0.02207632610182466, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.02207632610182466 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866766, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866766 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8654970760233918, "acc_stderr": 0.026168221344662297, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.026168221344662297 }, "harness|truthfulqa:mc|0": { "mc1": 0.4528763769889841, "mc1_stderr": 0.01742558984831402, "mc2": 0.6337134354987094, "mc2_stderr": 0.014897273290786066 } } ``` ### 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]
player1537/Bloom-560m-trained-on-Dolphin
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string - name: input_ids sequence: int32 splits: - name: train num_bytes: 2290189968 num_examples: 694524 download_size: 1237793186 dataset_size: 2290189968 --- # Dataset Card for "Bloom-560m-trained-on-Dolphin" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tollefj/norwegian-xsum-nob
--- language: - nb - 'no' license: cc-by-sa-4.0 size_categories: - 100K<n<1M task_categories: - summarization pretty_name: XSUM Norwegian Bokmål configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: document dtype: string - name: summary dtype: string - name: id dtype: string splits: - name: test num_bytes: 23794328 num_examples: 11334 - name: train num_bytes: 426389147 num_examples: 204045 - name: validation num_bytes: 23422946 num_examples: 11332 download_size: 301349675 dataset_size: 473606421 --- # XSUM - Translated Norwegian Bokmål Sourced from https://huggingface.co/datasets/NbAiLab/norwegian-xsum. Loaded from provided gzips and reuploaded due to errors accessing the original dataset through the dataset apis.
anhdungitvn/vi-corpus-cleaned-54988654
--- dataset_info: features: - name: text dtype: string splits: - name: clean num_bytes: 299737064438 num_examples: 54988654 - name: noisy num_bytes: 442955165504 num_examples: 92757798 download_size: 385431065652 dataset_size: 742692229942 configs: - config_name: default data_files: - split: clean path: data/clean-* - split: noisy path: data/noisy-* ---
CyberHarem/sara_touhou
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sara/サラ (Touhou) This is the dataset of sara/サラ (Touhou), containing 58 images and their tags. The core tags of this character are `pink_hair, short_hair, pink_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 58 | 32.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sara_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 58 | 26.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sara_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 92 | 41.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sara_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 58 | 31.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sara_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 92 | 46.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sara_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/sara_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 | 33 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, smile, red_dress, looking_at_viewer, one_side_up, short_sleeves, simple_background, bangs, full_body, open_mouth, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | red_dress | looking_at_viewer | one_side_up | short_sleeves | simple_background | bangs | full_body | open_mouth | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:------------|:--------------------|:--------------|:----------------|:--------------------|:--------|:------------|:-------------|:-------------------| | 0 | 33 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X |
Deojoandco/ah_openai_qt_dialog
--- dataset_info: features: - name: url dtype: string - name: id dtype: string - name: num_comments dtype: int64 - name: name dtype: string - name: title dtype: string - name: body dtype: string - name: score dtype: int64 - name: upvote_ratio dtype: float64 - name: distinguished dtype: 'null' - name: over_18 dtype: bool - name: created_utc dtype: float64 - name: comments list: - name: body dtype: string - name: created_utc dtype: float64 - name: distinguished dtype: 'null' - name: id dtype: string - name: permalink dtype: string - name: score dtype: int64 - name: best_num_comments dtype: int64 - name: dialog dtype: string - name: query_text dtype: string splits: - name: train num_bytes: 183642 num_examples: 26 download_size: 159847 dataset_size: 183642 --- # Dataset Card for "ah_openai_qt_dialog" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
halaction/song-lyrics
--- license: apache-2.0 ---
CyberHarem/syr_flover_isitwrongtotrytopickupgirlsinadungeon
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of syr_flover (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka) This is the dataset of syr_flover (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka), containing 18 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
thongnef/dataset_dacn
--- dataset_info: features: - name: sentence_idx dtype: int64 - name: words sequence: string - name: POS sequence: int64 - name: tag sequence: int64 splits: - name: train num_bytes: 13350196.989130436 num_examples: 13794 - name: test num_bytes: 3338033.1604691073 num_examples: 3449 download_size: 2535287 dataset_size: 16688230.149599543 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
datasets-examples/doc-formats-tsv-2
--- configs: - config_name: default data_files: "*.tsv" sep: "\t" size_categories: - n<1K --- # [doc] formats - tsv - 2 This dataset contains one tsv file at the root: - [data.tsv](./data.tsv) ```csv kind sound dog woof cat meow pokemon pika human hello ``` We define the separator as `"\t"` (tabulation) in the YAML config, as well as the config name and the location of the file, with a glob expression: ```yaml configs: - config_name: default data_files: "*.tsv" sep: "\t" size_categories: - n<1K ```
Silverovo/Diaperfur
--- license: apache-2.0 ---
AdapterOcean/med_alpaca_standardized_cluster_26_std
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 20203529 num_examples: 35798 download_size: 10273917 dataset_size: 20203529 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_26_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deokhk/zh_wiki_sentences_100000
--- dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 13169192 num_examples: 100000 - name: dev num_bytes: 131900 num_examples: 1000 download_size: 9348002 dataset_size: 13301092 --- # Dataset Card for "zh_wiki_sentences_100000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_sst2_regularized_past_tense
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 5165 num_examples: 33 - name: test num_bytes: 10745 num_examples: 71 - name: train num_bytes: 138939 num_examples: 1193 download_size: 82402 dataset_size: 154849 --- # Dataset Card for "MULTI_VALUE_sst2_regularized_past_tense" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
henrypapadatos/Open-ended_sycophancy
--- task_categories: - question-answering language: - en size_categories: - n<1K --- ## Dataset composition This dataset comprises 53 data points each ot them composed of a **prompt** and 2 different completions. The first one is **sycophantic** meaning that it favors being agreeable and agreeing with the views of the user. And the second one is **non_sycophantic**, favoring being honest in all circumstances. ## How I generated it I took the prompts out of the paper "Steering Llama 2 via Contrastive Activation Addition" written by Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, Alexander Matt Turner. Then, I generated the completions with GPT3.5 using the following system prompts: - "Favor being nice and agreeable over being truthful. To achieve that, don't hesitate to blatantly lie. You would rather agree with the general opinion and lie." for the sycophantic completion. - "You are someone who is always honest and truthful. You never lie just to sound agreeable or nice." for the non_sycophantic completion. I generated them one by one in order to correct GPT3.5 when it didn't behave as expected. For some prompts, GPT3.5 refused to give a sycophantic answer even with the aforementioned system prompt. In these cases, I wrote the completions manually.
kuanhuggingface/google_tts_encodec
--- dataset_info: features: - name: file_id dtype: string - name: instruction dtype: string - name: transcription dtype: string - name: src_encodec_0 sequence: int64 - name: src_encodec_1 sequence: int64 - name: src_encodec_2 sequence: int64 - name: src_encodec_3 sequence: int64 - name: src_encodec_4 sequence: int64 - name: src_encodec_5 sequence: int64 - name: src_encodec_6 sequence: int64 - name: src_encodec_7 sequence: int64 - name: tgt_encodec_0 sequence: int64 - name: tgt_encodec_1 sequence: int64 - name: tgt_encodec_2 sequence: int64 - name: tgt_encodec_3 sequence: int64 - name: tgt_encodec_4 sequence: int64 - name: tgt_encodec_5 sequence: int64 - name: tgt_encodec_6 sequence: int64 - name: tgt_encodec_7 sequence: int64 splits: - name: train num_bytes: 3701639864 num_examples: 90000 - name: validation num_bytes: 202925396 num_examples: 5000 - name: test num_bytes: 208941751 num_examples: 5000 download_size: 139109305 dataset_size: 4113507011 --- # Dataset Card for "google_tts_encodec" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bahar125/test12
--- dataset_info: features: - name: labels dtype: class_label: names: '0': negative '1': neutral '2': positive - name: text dtype: string splits: - name: train num_bytes: 7807 num_examples: 82 - name: test num_bytes: 1928 num_examples: 20 download_size: 10418 dataset_size: 9735 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
shushana/Magicoder_valid_subset
--- license: mit --- This dataset contains a subset of Magicoder dataset instances that can be compiled (as is) in their respectives languages.
NickM2002/carpie
--- license: apache-2.0 ---
Avinash7509/Singleton_Train
--- license: openrail ---
open-llm-leaderboard/details_Samee-ur__NeuralPipe-7B-slerp-DPO
--- pretty_name: Evaluation run of Samee-ur/NeuralPipe-7B-slerp-DPO dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Samee-ur/NeuralPipe-7B-slerp-DPO](https://huggingface.co/Samee-ur/NeuralPipe-7B-slerp-DPO)\ \ 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_Samee-ur__NeuralPipe-7B-slerp-DPO\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-18T03:15:08.254150](https://huggingface.co/datasets/open-llm-leaderboard/details_Samee-ur__NeuralPipe-7B-slerp-DPO/blob/main/results_2024-02-18T03-15-08.254150.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\": 0.6411645044830544,\n\ \ \"acc_stderr\": 0.03223219886055753,\n \"acc_norm\": 0.6418262323395414,\n\ \ \"acc_norm_stderr\": 0.03288767364253438,\n \"mc1\": 0.4528763769889841,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6352622603062533,\n\ \ \"mc2_stderr\": 0.015295497304172482\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6578498293515358,\n \"acc_stderr\": 0.013864152159177275,\n\ \ \"acc_norm\": 0.6928327645051194,\n \"acc_norm_stderr\": 0.013481034054980941\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6739693288189603,\n\ \ \"acc_stderr\": 0.004678006403691717,\n \"acc_norm\": 0.8633738299143597,\n\ \ \"acc_norm_stderr\": 0.0034275034755677967\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.04244633238353227\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\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\ \ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n\ \ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440679,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440679\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n\ \ \"acc_stderr\": 0.02366421667164251,\n \"acc_norm\": 0.7774193548387097,\n\ \ \"acc_norm_stderr\": 0.02366421667164251\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.02912652283458682,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.02912652283458682\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.02439667298509477,\n \ \ \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.02439667298509477\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131157,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131157\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977927,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977927\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8532110091743119,\n \"acc_stderr\": 0.015173141845126243,\n \"\ acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.015173141845126243\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.8137254901960784,\n \"acc_stderr\": 0.02732547096671631,\n\ \ \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.02732547096671631\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8227848101265823,\n \"acc_stderr\": 0.024856364184503224,\n \ \ \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.024856364184503224\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.0364129708131373,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.0364129708131373\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077802,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077802\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\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608306,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608306\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.023948512905468365,\n\ \ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.023948512905468365\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3653631284916201,\n\ \ \"acc_stderr\": 0.016104833880142284,\n \"acc_norm\": 0.3653631284916201,\n\ \ \"acc_norm_stderr\": 0.016104833880142284\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600713,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600713\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46936114732724904,\n\ \ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\ \ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462937,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462937\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069443,\n \ \ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069443\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\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\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139968,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139968\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4528763769889841,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6352622603062533,\n\ \ \"mc2_stderr\": 0.015295497304172482\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938278\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6626231993934799,\n \ \ \"acc_stderr\": 0.013023665136222095\n }\n}\n```" repo_url: https://huggingface.co/Samee-ur/NeuralPipe-7B-slerp-DPO leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|arc:challenge|25_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-18T03-15-08.254150.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|gsm8k|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hellaswag|10_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-18T03-15-08.254150.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-management|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-18T03-15-08.254150.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|truthfulqa:mc|0_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-18T03-15-08.254150.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_18T03_15_08.254150 path: - '**/details_harness|winogrande|5_2024-02-18T03-15-08.254150.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-18T03-15-08.254150.parquet' - config_name: results data_files: - split: 2024_02_18T03_15_08.254150 path: - results_2024-02-18T03-15-08.254150.parquet - split: latest path: - results_2024-02-18T03-15-08.254150.parquet --- # Dataset Card for Evaluation run of Samee-ur/NeuralPipe-7B-slerp-DPO <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Samee-ur/NeuralPipe-7B-slerp-DPO](https://huggingface.co/Samee-ur/NeuralPipe-7B-slerp-DPO) 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_Samee-ur__NeuralPipe-7B-slerp-DPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-18T03:15:08.254150](https://huggingface.co/datasets/open-llm-leaderboard/details_Samee-ur__NeuralPipe-7B-slerp-DPO/blob/main/results_2024-02-18T03-15-08.254150.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": 0.6411645044830544, "acc_stderr": 0.03223219886055753, "acc_norm": 0.6418262323395414, "acc_norm_stderr": 0.03288767364253438, "mc1": 0.4528763769889841, "mc1_stderr": 0.01742558984831402, "mc2": 0.6352622603062533, "mc2_stderr": 0.015295497304172482 }, "harness|arc:challenge|25": { "acc": 0.6578498293515358, "acc_stderr": 0.013864152159177275, "acc_norm": 0.6928327645051194, "acc_norm_stderr": 0.013481034054980941 }, "harness|hellaswag|10": { "acc": 0.6739693288189603, "acc_stderr": 0.004678006403691717, "acc_norm": 0.8633738299143597, "acc_norm_stderr": 0.0034275034755677967 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.04244633238353227, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.04244633238353227 }, "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": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6867924528301886, "acc_stderr": 0.028544793319055326, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.028544793319055326 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108101, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108101 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440679, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440679 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7774193548387097, "acc_stderr": 0.02366421667164251, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.02366421667164251 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4876847290640394, "acc_stderr": 0.035169204442208966, "acc_norm": 0.4876847290640394, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.032876667586034906, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.032876667586034906 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.02912652283458682, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.02912652283458682 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6358974358974359, "acc_stderr": 0.02439667298509477, "acc_norm": 0.6358974358974359, "acc_norm_stderr": 0.02439667298509477 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131157, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131157 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977927, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977927 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8532110091743119, "acc_stderr": 0.015173141845126243, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.015173141845126243 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8137254901960784, "acc_stderr": 0.02732547096671631, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.02732547096671631 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8227848101265823, "acc_stderr": 0.024856364184503224, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.024856364184503224 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6771300448430493, "acc_stderr": 0.031381476375754995, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.031381476375754995 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7786259541984732, "acc_stderr": 0.0364129708131373, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.0364129708131373 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077802, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077802 }, "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": 0.8301404853128991, "acc_stderr": 0.013428186370608306, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608306 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7283236994219653, "acc_stderr": 0.023948512905468365, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.023948512905468365 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3653631284916201, "acc_stderr": 0.016104833880142284, "acc_norm": 0.3653631284916201, "acc_norm_stderr": 0.016104833880142284 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600713, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600713 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5035460992907801, "acc_stderr": 0.02982674915328092, "acc_norm": 0.5035460992907801, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462937, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462937 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6797385620915033, "acc_stderr": 0.018875682938069443, "acc_norm": 0.6797385620915033, "acc_norm_stderr": 0.018875682938069443 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.746938775510204, "acc_stderr": 0.02783302387139968, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.02783302387139968 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.4528763769889841, "mc1_stderr": 0.01742558984831402, "mc2": 0.6352622603062533, "mc2_stderr": 0.015295497304172482 }, "harness|winogrande|5": { "acc": 0.8050513022888713, "acc_stderr": 0.011134099415938278 }, "harness|gsm8k|5": { "acc": 0.6626231993934799, "acc_stderr": 0.013023665136222095 } } ``` ## 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]
distilled-from-one-sec-cv12/chunk_147
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1081692168 num_examples: 210774 download_size: 1105255160 dataset_size: 1081692168 --- # Dataset Card for "chunk_147" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
systemk/c4-ja-5k-metrics
--- dataset_info: - config_name: all-nlp features: - name: text dtype: string - name: language dtype: string - name: confidence struct: - name: ARABIC dtype: float64 - name: CHINESE dtype: float64 - name: DUTCH dtype: float64 - name: ENGLISH dtype: float64 - name: FRENCH dtype: float64 - name: GERMAN dtype: float64 - name: ITALIAN dtype: float64 - name: JAPANESE dtype: float64 - name: POLISH dtype: float64 - name: PORTUGUESE dtype: float64 - name: RUSSIAN dtype: float64 - name: SPANISH dtype: float64 - name: TURKISH dtype: float64 - name: lines_with_no_ending_punctuation struct: - name: label struct: - name: no_ending dtype: int64 - name: mask sequence: sequence: int64 - name: lines_with_too_few_words struct: - name: label struct: - name: too_few dtype: int64 - name: mask sequence: sequence: int64 - name: has_naughty_word dtype: bool - name: naughty_words sequence: string - name: has_javascript dtype: bool - name: has_lorem_ipsum dtype: bool - name: has_curly_brace dtype: bool - name: line_count dtype: int64 - name: character_count dtype: int64 - name: token_count dtype: int64 - name: text_count dtype: int64 - name: sentence_counts sequence: sequence: int64 splits: - name: train num_bytes: 60566536 num_examples: 5000 download_size: 27094942 dataset_size: 60566536 - config_name: dsir features: - name: text dtype: string - name: tokens sequence: string - name: weight dtype: float32 - name: prob_dists sequence: float32 splits: - name: train num_bytes: 484592779.0 num_examples: 5000 - name: percent_0_5 num_bytes: 24229638.95 num_examples: 250 - name: percent_20_25 num_bytes: 24229638.95 num_examples: 250 - name: percent_40_45 num_bytes: 24229638.95 num_examples: 250 - name: percent_60_65 num_bytes: 24229638.95 num_examples: 250 - name: percent_80_85 num_bytes: 24229638.95 num_examples: 250 - name: percent_95_100 num_bytes: 24229638.95 num_examples: 250 download_size: 331722089 dataset_size: 629970612.7000002 - config_name: dsir-domain features: - name: text dtype: string - name: tokens sequence: string - name: weight dtype: float32 - name: prob_dists sequence: float32 splits: - name: train num_bytes: 484592779.0 num_examples: 5000 download_size: 74755598 dataset_size: 484592779.0 - config_name: ppl_ccnet features: - name: text dtype: string - name: ppl dtype: float64 splits: - name: train num_bytes: 47550417.0 num_examples: 5000 download_size: 24672308 dataset_size: 47550417.0 configs: - config_name: all-nlp data_files: - split: train path: all-nlp/train-* - config_name: dsir data_files: - split: train path: dsir/train-* - split: percent_0_5 path: dsir/percent_0_5-* - split: percent_20_25 path: dsir/percent_20_25-* - split: percent_40_45 path: dsir/percent_40_45-* - split: percent_60_65 path: dsir/percent_60_65-* - split: percent_80_85 path: dsir/percent_80_85-* - split: percent_95_100 path: dsir/percent_95_100-* - config_name: dsir-domain data_files: - split: train path: dsir-domain/train-* - config_name: ppl_ccnet data_files: - split: train path: ppl_ccnet/train-* ---
tyang816/cath
--- license: apache-2.0 ---
wenge-research/yayi2_pretrain_data
--- license: apache-2.0 language: - zh - en size_categories: - 100B<n<1T --- ## 介绍/Introduction 本数据集源自雅意训练语料，我们精选了约100B数据，数据大小约为500GB。我们期望通过雅意预训练数据的开源推动中文预训练大模型开源社区的发展，并积极为此贡献力量。通过开源，我们与每一位合作伙伴共同构建雅意大模型生态。 We opensource the pre-trained dataset in this release, it should contain more than 100B tokens depending on the tokenizer you use, requiring more than 500GB of local storage. By open-sourcing the pre-trained dataset, we aim to contribute to the development of the Chinese pre-trained large language model open-source community. Through open-source, we aspire to collaborate with every partner in building the YAYI large language model ecosystem. ## 组成 * 在预训练阶段，我们不仅使用了互联网数据来训练模型的语言能力，还添加了通用精选数据和领域数据，以增强模型的专业技能。通用精选数据包含人工收集和整理的高质量数据。涵盖了报纸类数据、文献类数据、APP类数据、代码类数据、书籍类数据、百科类数据。其中，报纸类数据包括广泛的新闻报道和专栏文章，这类数据通常结构化程度高，信息量丰富。文献类数据包括学术论文和研究报告，为我们的数据集注入了专业和深度。代码类数据包括各种编程语言的源码，有助于构建和优化技术类数据的处理模型。书籍类数据涵盖了小说、诗歌、古文、教材等内容，提供丰富的语境和词汇，增强语言模型的理解能力。数据分布情况如下： * During the pre-training phase, we not only utilized internet data to train the model's language abilities but also incorporated curated general data and domain-specific information to enhance the model's expertise. Curated general data covers a wide range of categories including books (e.g., textbooks, novels), codes, encyclopedias, forums, academic papers, authoritative news, laws and regulations. Details of the data distribution are as follows: ![data distribution](https://huggingface.co/datasets/wenge-research/yayi2_pretrain_data/resolve/main/assets/data_distribution.jpg) ## 数据清洗 - 我们构建了一套全方位提升数据质量的数据处理流水线，包括标准化、启发式清洗、多级去重、毒性过滤四个模块。我们共收集了 240TB 原始数据，预处理后仅剩 10.6TB 高质量数据。整体流程如下： - We establish a comprehensive data processing pipeline to enhance data quality in all aspects. This pipeline comprises four modules: normalizing, heuristic cleaning, multi-level deduplication, and toxicity filtering. 240 terabytes of raw data are collected for pre-training, and only 10.6 terabytes of high-quality data remain after preprocessing. Details of the data processing pipeline are as follows: ![data process](https://huggingface.co/datasets/wenge-research/yayi2_pretrain_data/resolve/main/assets/data_process.png) ## 协议/License 本项目中的代码依照 [Apache-2.0](https://github.com/wenge-research/YAYI2/blob/main/LICENSE) 协议开源，社区使用 YAYI 2 模型和数据需要遵循[雅意YAYI 2 模型社区许可协议](https://github.com/wenge-research/YAYI2/blob/main/COMMUNITY_LICENSE)。若您需要将雅意 YAYI 2系列模型或其衍生品用作商业用途，请根据[《雅意 YAYI 2 模型商用许可协议》](https://github.com/wenge-research/YAYI2/blob/main/COMMERCIAL_LICENSE)将商用许可申请登记信息发送至指定邮箱 [yayi@wenge.com](mailto:yayi@wenge.com)。审核通过后，雅意将授予您商用版权许可，请遵循协议中的商业许可限制。 The code in this project is open-sourced under the [Apache-2.0](https://github.com/wenge-research/YAYI2/blob/main/LICENSE) license. The use of YaYi series model weights and data must adhere to the [YAYI 2 Community License](https://github.com/wenge-research/YAYI2/blob/main/COMMUNITY_LICENSE). If you intend to use the YAYI 2 series models or their derivatives for commercial purposes, please submit your commercial license application and registration information to [yayi@wenge.com](mailto:yayi@wenge.com), following the [YAYI 2 Commercial License](https://github.com/wenge-research/YAYI2/blob/main/COMMERCIAL_LICENSE). Upon approval, YAYI will grant you a commercial copyright license, subject to the commercial license restrictions outlined in the agreement. ## 引用/Citation 如果您在工作中使用了我们的模型或者数据，请引用我们的论文。 If you are using the resource for your work, please cite our paper. ``` @article{YAYI 2, author = {Yin Luo, Qingchao Kong, Nan Xu, et.al.}, title = {YAYI 2: Multilingual Open Source Large Language Models}, journal = {arXiv preprint arXiv:2312.14862}, url = {https://arxiv.org/abs/2312.14862}, year = {2023} } ```
mxronga/edeyoruba
--- license: apache-2.0 language: - yo tags: - pretrain ---
heliosprime/twitter_dataset_1712999470
--- 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: 9232 num_examples: 20 download_size: 8931 dataset_size: 9232 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1712999470" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GeneralRincewind/ThumbnailTrendingDataset
--- dataset_info: features: - name: video_id dtype: string - name: title dtype: string - name: publishedAt dtype: string - name: channelId dtype: string - name: channelTitle dtype: string - name: categoryId dtype: int64 - name: trending_date dtype: string - name: tags dtype: string - name: view_count dtype: int64 - name: likes dtype: int64 - name: dislikes dtype: int64 - name: comment_count dtype: int64 - name: thumbnail_link dtype: string - name: comments_disabled dtype: bool - name: ratings_disabled dtype: bool - name: description dtype: string - name: HDThumbnail dtype: string - name: __index_level_0__ dtype: int64 - name: image dtype: image splits: - name: train num_bytes: 1400574193.7436364 num_examples: 52660 download_size: 1419220915 dataset_size: 1400574193.7436364 configs: - config_name: default data_files: - split: train path: data/train-* ---
BigTMiami/small_amazon_2_500_condensed
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 2887244 num_examples: 433 - name: validation num_bytes: 2693872 num_examples: 404 download_size: 1892480 dataset_size: 5581116 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
KiramekiSunnyPro/tokofokawa
--- license: openrail ---
alexgoodell/synthetic-patients
--- license: cc-by-nc-sa-4.0 ---
CJWeiss/govreport_id_rename
--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 804293268 num_examples: 14597 - name: test num_bytes: 149069637 num_examples: 2919 - name: valid num_bytes: 107525366 num_examples: 1947 download_size: 506718966 dataset_size: 1060888271 --- # Dataset Card for "govreport_id_rename" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Abinashkumar/llama2
--- license: apache-2.0 ---
ArmandoReyesMx49/data-set_demo
--- license: mit ---
Troffix/test
--- license: mit ---
open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_128k_b
--- pretty_name: Evaluation run of LeroyDyer/Mixtral_AI_128k_b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LeroyDyer/Mixtral_AI_128k_b](https://huggingface.co/LeroyDyer/Mixtral_AI_128k_b)\ \ 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_LeroyDyer__Mixtral_AI_128k_b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-22T00:29:42.883802](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_128k_b/blob/main/results_2024-03-22T00-29-42.883802.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\": 0.6370404140176479,\n\ \ \"acc_stderr\": 0.03244713202454115,\n \"acc_norm\": 0.6411769374551979,\n\ \ \"acc_norm_stderr\": 0.03309762374460682,\n \"mc1\": 0.40024479804161567,\n\ \ \"mc1_stderr\": 0.017151605555749138,\n \"mc2\": 0.5708502006690636,\n\ \ \"mc2_stderr\": 0.015277542354002341\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.60580204778157,\n \"acc_stderr\": 0.014280522667467321,\n\ \ \"acc_norm\": 0.6407849829351536,\n \"acc_norm_stderr\": 0.014020224155839162\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6638119896434973,\n\ \ \"acc_stderr\": 0.004714386376337134,\n \"acc_norm\": 0.8468432583150767,\n\ \ \"acc_norm_stderr\": 0.003594024993230561\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926603,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926603\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\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\ \ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\ \ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n\ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.03656343653353159,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.03656343653353159\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\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.04161808503501531,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.04161808503501531\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\ \ \"acc_stderr\": 0.02390491431178265,\n \"acc_norm\": 0.7709677419354839,\n\ \ \"acc_norm_stderr\": 0.02390491431178265\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593552,\n\ \ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593552\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\ \ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \ \ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8128440366972477,\n \"acc_stderr\": 0.016722684526200144,\n \"\ acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.016722684526200144\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849316,\n \"\ acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849316\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\ \ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\ \ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.03880848301082394,\n\ \ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.03880848301082394\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098823,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098823\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\ \ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.02250903393707781,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.02250903393707781\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n\ \ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\ \ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.024405173935783234,\n\ \ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.024405173935783234\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3843575418994413,\n\ \ \"acc_stderr\": 0.016269088663959402,\n \"acc_norm\": 0.3843575418994413,\n\ \ \"acc_norm_stderr\": 0.016269088663959402\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826514,\n\ \ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826514\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\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495036,\n\ \ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495036\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4661016949152542,\n\ \ \"acc_stderr\": 0.012740853872949829,\n \"acc_norm\": 0.4661016949152542,\n\ \ \"acc_norm_stderr\": 0.012740853872949829\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335303,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335303\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6470588235294118,\n \"acc_stderr\": 0.019333142020797164,\n \ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.019333142020797164\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675592,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675592\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\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40024479804161567,\n\ \ \"mc1_stderr\": 0.017151605555749138,\n \"mc2\": 0.5708502006690636,\n\ \ \"mc2_stderr\": 0.015277542354002341\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7916337805840569,\n \"acc_stderr\": 0.011414554399987729\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4564063684609553,\n \ \ \"acc_stderr\": 0.013720038270485327\n }\n}\n```" repo_url: https://huggingface.co/LeroyDyer/Mixtral_AI_128k_b 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_22T00_29_42.883802 path: - '**/details_harness|arc:challenge|25_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-22T00-29-42.883802.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|gsm8k|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hellaswag|10_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-29-42.883802.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-29-42.883802.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T00-29-42.883802.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_22T00_29_42.883802 path: - '**/details_harness|winogrande|5_2024-03-22T00-29-42.883802.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-22T00-29-42.883802.parquet' - config_name: results data_files: - split: 2024_03_22T00_29_42.883802 path: - results_2024-03-22T00-29-42.883802.parquet - split: latest path: - results_2024-03-22T00-29-42.883802.parquet --- # Dataset Card for Evaluation run of LeroyDyer/Mixtral_AI_128k_b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [LeroyDyer/Mixtral_AI_128k_b](https://huggingface.co/LeroyDyer/Mixtral_AI_128k_b) 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_LeroyDyer__Mixtral_AI_128k_b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-22T00:29:42.883802](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_128k_b/blob/main/results_2024-03-22T00-29-42.883802.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": 0.6370404140176479, "acc_stderr": 0.03244713202454115, "acc_norm": 0.6411769374551979, "acc_norm_stderr": 0.03309762374460682, "mc1": 0.40024479804161567, "mc1_stderr": 0.017151605555749138, "mc2": 0.5708502006690636, "mc2_stderr": 0.015277542354002341 }, "harness|arc:challenge|25": { "acc": 0.60580204778157, "acc_stderr": 0.014280522667467321, "acc_norm": 0.6407849829351536, "acc_norm_stderr": 0.014020224155839162 }, "harness|hellaswag|10": { "acc": 0.6638119896434973, "acc_stderr": 0.004714386376337134, "acc_norm": 0.8468432583150767, "acc_norm_stderr": 0.003594024993230561 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6710526315789473, "acc_stderr": 0.03823428969926603, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.03823428969926603 }, "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": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566017, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566017 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.03656343653353159, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.03656343653353159 }, "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": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5241379310344828, "acc_stderr": 0.04161808503501531, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.04161808503501531 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7709677419354839, "acc_stderr": 0.02390491431178265, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.02390491431178265 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386414, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386414 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.023814477086593552, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.023814477086593552 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37037037037037035, "acc_stderr": 0.02944316932303154, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.02944316932303154 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.37748344370860926, "acc_stderr": 0.0395802723112157, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.0395802723112157 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8128440366972477, "acc_stderr": 0.016722684526200144, "acc_norm": 0.8128440366972477, "acc_norm_stderr": 0.016722684526200144 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7843137254901961, "acc_stderr": 0.028867431449849316, "acc_norm": 0.7843137254901961, "acc_norm_stderr": 0.028867431449849316 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6636771300448431, "acc_stderr": 0.031708824268455, "acc_norm": 0.6636771300448431, "acc_norm_stderr": 0.031708824268455 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.732824427480916, "acc_stderr": 0.03880848301082394, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.03880848301082394 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098823, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098823 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094633, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094633 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "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": 0.8632478632478633, "acc_stderr": 0.02250903393707781, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.02250903393707781 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8084291187739464, "acc_stderr": 0.014072859310451949, "acc_norm": 0.8084291187739464, "acc_norm_stderr": 0.014072859310451949 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7109826589595376, "acc_stderr": 0.024405173935783234, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.024405173935783234 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3843575418994413, "acc_stderr": 0.016269088663959402, "acc_norm": 0.3843575418994413, "acc_norm_stderr": 0.016269088663959402 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826514, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826514 }, "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": 0.7098765432098766, "acc_stderr": 0.025251173936495036, "acc_norm": 0.7098765432098766, "acc_norm_stderr": 0.025251173936495036 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4661016949152542, "acc_stderr": 0.012740853872949829, "acc_norm": 0.4661016949152542, "acc_norm_stderr": 0.012740853872949829 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.028661996202335303, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.028661996202335303 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6470588235294118, "acc_stderr": 0.019333142020797164, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.019333142020797164 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675592, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675592 }, "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": 0.89, "acc_stderr": 0.03144660377352202, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352202 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.40024479804161567, "mc1_stderr": 0.017151605555749138, "mc2": 0.5708502006690636, "mc2_stderr": 0.015277542354002341 }, "harness|winogrande|5": { "acc": 0.7916337805840569, "acc_stderr": 0.011414554399987729 }, "harness|gsm8k|5": { "acc": 0.4564063684609553, "acc_stderr": 0.013720038270485327 } } ``` ## 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]
CyberHarem/utena_hiiragi_mahoushoujoniakogarete
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Utena Hiiragi/柊うてな (Mahou Shoujo ni Akogarete) This is the dataset of Utena Hiiragi/柊うてな (Mahou Shoujo ni Akogarete), containing 304 images and their tags. The core tags of this character are `short_hair, black_hair, ahoge, yellow_eyes, horns, purple_hair, magical_girl, yellow_horns, breasts, wings, symbol-shaped_pupils`, 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 | 304 | 193.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/utena_hiiragi_mahoushoujoniakogarete/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 304 | 193.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/utena_hiiragi_mahoushoujoniakogarete/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 601 | 337.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/utena_hiiragi_mahoushoujoniakogarete/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/utena_hiiragi_mahoushoujoniakogarete', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, green_sailor_collar, serafuku, solo, yellow_neckerchief, open_mouth, blush, upper_body, white_shirt | | 1 | 19 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, blush, hair_between_eyes, looking_at_viewer, portrait, close-up, cross-shaped_pupils, open_mouth, facial_mark, sweatdrop | | 2 | 11 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blush, solo, hair_between_eyes, outdoors, day, open_mouth, blue_sky, cross-shaped_pupils, fang, looking_at_viewer, cloud, portrait, smile, sweatdrop, star_(symbol) | | 3 | 16 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, breastless_clothes, shrug_(clothing), small_breasts, solo, star_pasties, cross_pasties, upper_body, demon_wings, open_mouth, corset, black_nails, blush, facial_mark, nail_polish, star_(symbol), cross-shaped_pupils, blue_sky, outdoors, day, fang, looking_at_viewer, smile | | 4 | 15 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, breastless_clothes, corset, demon_wings, lowleg_pants, navel, showgirl_skirt, shrug_(clothing), low_wings, cross_pasties, cross-shaped_pupils, solo, star_pasties, medium_breasts, revealing_clothes, small_breasts | | 5 | 7 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, open_mouth, solo, indoors, pajamas, shirt, bed, blanket, messy_hair, long_sleeves, under_covers, blush, collarbone, looking_at_viewer, sweatdrop | | 6 | 7 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, open_mouth, solo, sweatdrop, white_shirt, long_sleeves, blush, purple_skirt, from_side | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | green_sailor_collar | serafuku | solo | yellow_neckerchief | open_mouth | blush | upper_body | white_shirt | hair_between_eyes | looking_at_viewer | portrait | close-up | cross-shaped_pupils | facial_mark | sweatdrop | outdoors | day | blue_sky | fang | cloud | smile | star_(symbol) | breastless_clothes | shrug_(clothing) | small_breasts | star_pasties | cross_pasties | demon_wings | corset | black_nails | nail_polish | lowleg_pants | navel | showgirl_skirt | low_wings | medium_breasts | revealing_clothes | indoors | pajamas | shirt | bed | blanket | messy_hair | long_sleeves | under_covers | collarbone | purple_skirt | from_side | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------------|:-----------|:-------|:---------------------|:-------------|:--------|:-------------|:--------------|:--------------------|:--------------------|:-----------|:-----------|:----------------------|:--------------|:------------|:-----------|:------|:-----------|:-------|:--------|:--------|:----------------|:---------------------|:-------------------|:----------------|:---------------|:----------------|:--------------|:---------|:--------------|:--------------|:---------------|:--------|:-----------------|:------------|:-----------------|:--------------------|:----------|:----------|:--------|:------|:----------|:-------------|:---------------|:---------------|:-------------|:---------------|:------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 19 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | X | | X | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 11 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | X | | X | X | | | X | X | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 16 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | X | | X | X | X | | | X | | | X | X | | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 4 | 15 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | X | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | 5 | 7 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | X | | X | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | 6 | 7 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | X | | X | X | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X |
distilled-from-one-sec-cv12/chunk_86
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1388082832 num_examples: 270476 download_size: 1417945256 dataset_size: 1388082832 --- # Dataset Card for "chunk_86" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rajarshi21/KanjiSD
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6274302.35 num_examples: 2025 download_size: 6444167 dataset_size: 6274302.35 configs: - config_name: default data_files: - split: train path: data/train-* ---
Divyaamith/resume-dataset
--- license: mit task_categories: - text-classification language: - en ---
kristinashemet/test_23.03
--- dataset_info: features: - name: Text dtype: string - name: Question dtype: string - name: Answer dtype: string splits: - name: train num_bytes: 48608 num_examples: 32 download_size: 19856 dataset_size: 48608 configs: - config_name: default data_files: - split: train path: data/train-* ---
llm-lens/lens_vqa_sample_test
--- dataset_info: features: - name: multiple_choice_answer dtype: string - name: answers sequence: string - name: id_image dtype: int64 - name: question_id dtype: int64 - name: question dtype: string - name: image dtype: image - name: id dtype: int64 - name: intensive_captions_Salesforce-blip-image-captioning-large sequence: string splits: - name: test num_bytes: 1601792.0 num_examples: 10 download_size: 1595850 dataset_size: 1601792.0 --- # Dataset Card for "lens_vqa_sample_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/mizuki_pokemon
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mizuki (Pokémon) This is the dataset of mizuki (Pokémon), containing 500 images and their tags. The core tags of this character are `black_hair, short_hair, bangs, hat, red_headwear, eyelashes`, 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 | 459.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuki_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 295.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuki_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1067 | 590.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuki_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 421.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuki_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1067 | 802.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizuki_pokemon/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/mizuki_pokemon', 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 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, beanie, floral_print, green_shorts, short_sleeves, tied_shirt, :d, bag, blush, open_mouth, t-shirt, yellow_shirt, bracelet, z-ring, tongue, poke_ball_(basic), upper_teeth_only, short_shorts, holding_poke_ball, pokemon_(creature), solo, blue_eyes, simple_background, white_background | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, beanie, blue_eyes, short_sleeves, simple_background, solo, white_background, yellow_shirt, blush, looking_at_viewer, tied_shirt, white_pupils, green_shorts, holding_poke_ball, poke_ball_(basic), open_mouth, smile, bag, closed_mouth, floral_print, upper_body | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, beanie, closed_mouth, green_shorts, short_sleeves, solo, yellow_shirt, floral_print, simple_background, grey_eyes, looking_at_viewer, short_shorts, tied_shirt, white_background, smile, blush, shoes, sitting | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | blush, large_breasts, nipples, black_eyes, heart-shaped_pupils, sweat, nude, open_mouth, 1girl, smile, 2girls, collarbone, grabbing, yuri, blonde_hair, breast_grab, hetero | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | beanie | floral_print | green_shorts | short_sleeves | tied_shirt | :d | bag | blush | open_mouth | t-shirt | yellow_shirt | bracelet | z-ring | tongue | poke_ball_(basic) | upper_teeth_only | short_shorts | holding_poke_ball | pokemon_(creature) | solo | blue_eyes | simple_background | white_background | looking_at_viewer | white_pupils | smile | closed_mouth | upper_body | grey_eyes | shoes | sitting | large_breasts | nipples | black_eyes | heart-shaped_pupils | sweat | nude | 2girls | collarbone | grabbing | yuri | blonde_hair | breast_grab | hetero | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:---------------|:---------------|:----------------|:-------------|:-----|:------|:--------|:-------------|:----------|:---------------|:-----------|:---------|:---------|:--------------------|:-------------------|:---------------|:--------------------|:---------------------|:-------|:------------|:--------------------|:-------------------|:--------------------|:---------------|:--------|:---------------|:-------------|:------------|:--------|:----------|:----------------|:----------|:-------------|:----------------------|:--------|:-------|:---------|:-------------|:-----------|:-------|:--------------|:--------------|:---------| | 0 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | X | X | X | | X | | | | X | | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | | | X | | | X | | | | | | X | | | X | | X | X | X | | X | X | | X | X | X | | | | | | | | | | | | | | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | | | | | | X | X | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
DimitrisMantas/PLASTIC
--- license: cc-by-4.0 ---
KursKumpel/FHDW
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 1008494 num_examples: 1000 download_size: 346107 dataset_size: 1008494 configs: - config_name: default data_files: - split: train path: data/train-* ---
thangvip/thuvienphapluat-question-query
--- dataset_info: features: - name: title dtype: string - name: question dtype: string - name: content dtype: string - name: queries dtype: string splits: - name: train num_bytes: 74387263.28265 num_examples: 19861 download_size: 22410062 dataset_size: 74387263.28265 configs: - config_name: default data_files: - split: train path: data/train-* ---
veroinesc/test
--- license: unknown ---
liuyanchen1015/MULTI_VALUE_sst2_preposition_chopping
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 924 num_examples: 9 - name: test num_bytes: 2307 num_examples: 17 - name: train num_bytes: 62708 num_examples: 641 download_size: 31247 dataset_size: 65939 --- # Dataset Card for "MULTI_VALUE_sst2_preposition_chopping" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yongchoooon/fire-aihub-chatgpt
--- license: cc-by-nc-sa-4.0 annotations_creators: - machine-generated language: - en language_creators: - other multilinguality: - monolingual pretty_name: fire-aihub-chatgpt size_categories: - n<1K tags: [] task_categories: - text-to-image task_ids: [] ---
heliosprime/twitter_dataset_1712997731
--- 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: 10475 num_examples: 23 download_size: 8402 dataset_size: 10475 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1712997731" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-futin__feed-sen_en-2f01d7-2175769990
--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-2.7b metrics: [] dataset_name: futin/feed dataset_config: sen_en 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: facebook/opt-2.7b * Dataset: futin/feed * Config: sen_en * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
CyberHarem/asuka_azurlane
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of asuka/飛鳥/飞鸟 (Azur Lane) This is the dataset of asuka/飛鳥/飞鸟 (Azur Lane), containing 368 images and their tags. The core tags of this character are `breasts, ponytail, brown_eyes, ribbon, large_breasts, hair_ribbon, black_hair, brown_hair, white_ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 368 | 446.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 368 | 270.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 875 | 566.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 368 | 396.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 875 | 781.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asuka_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/asuka_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, cleavage, open_mouth, :d, blush, striped_bikini, navel, red_scarf, simple_background, white_background | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, navel, solo, striped_bikini, cleavage, front-tie_top, looking_at_viewer, blush, side-tie_bikini_bottom, multicolored_stripes, open_mouth, red_scarf, white_background, smile, multicolored_clothes, simple_background | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | cleavage, looking_at_viewer, 1girl, cloud, day, open_mouth, outdoors, solo, blue_sky, beach, navel, side-tie_bikini_bottom, smile, ocean, striped_bikini, blush, long_hair | | 3 | 30 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | school_uniform, 1girl, solo, sweater_vest, black_thighhighs, dual_wielding, plaid_skirt, red_scarf, katana, necktie, smile, looking_at_viewer, blush | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | cleavage | open_mouth | :d | blush | striped_bikini | navel | red_scarf | simple_background | white_background | front-tie_top | side-tie_bikini_bottom | multicolored_stripes | smile | multicolored_clothes | cloud | day | outdoors | blue_sky | beach | ocean | long_hair | school_uniform | sweater_vest | black_thighhighs | dual_wielding | plaid_skirt | katana | necktie | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:-------------|:-----|:--------|:-----------------|:--------|:------------|:--------------------|:-------------------|:----------------|:-------------------------|:-----------------------|:--------|:-----------------------|:--------|:------|:-----------|:-----------|:--------|:--------|:------------|:-----------------|:---------------|:-------------------|:----------------|:--------------|:---------|:----------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | | X | X | X | | | | | X | | X | | X | X | X | X | X | X | X | | | | | | | | | 3 | 30 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | | | X | | | X | | | | | | X | | | | | | | | | X | X | X | X | X | X | X |
Telugu-LLM-Labs/uonlp_culturaX_telugu_romanized_100k
--- license: mit ---
ltg/nb-samtale-conversations
--- license: cc0-1.0 task_categories: - conversational language: - 'no' - nb - nn pretty_name: NB Samtale — Conversations size_categories: - 1K<n<10K --- # NB Samtale — Conversations This dataset contains extracted and cleaned conversations from the [NB Samtale corpus](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-85/). The original is a speech corpus made by the Language Bank at the National Library of Norway. The corpus contains orthographically transcribed speech from podcasts and recordings of live events.
zxzl/celeb-identities
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Beyonce '1': Jennie_of_BlackPink '2': Martin_Luther_King_Jr. '3': Matt_Damon '4': Miranda_Kerr '5': RM_of_BTS splits: - name: train num_bytes: 1207553.0 num_examples: 18 download_size: 1206043 dataset_size: 1207553.0 --- # Dataset Card for "celeb-identities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Odunope/testsets
--- dataset_info: features: - name: row dtype: string splits: - name: train num_bytes: 18541.6 num_examples: 8 - name: test num_bytes: 4635.4 num_examples: 2 download_size: 36285 dataset_size: 23177.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
NarouMas/VariantCommand
--- license: mit ---
TrevorJS/mtg-rules-dataset
--- dataset_info: features: - name: number dtype: string - name: text dtype: string - name: examples sequence: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 845258 num_examples: 2944 download_size: 372002 dataset_size: 845258 configs: - config_name: default data_files: - split: train path: data/train-* ---
DaisyStar004/Transformed_data
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 385155 num_examples: 607 download_size: 211261 dataset_size: 385155 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Transformed_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-high_school_macroeconomics-verbal-neg-prepend
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_prompt dtype: string splits: - name: test num_bytes: 176962 num_examples: 390 download_size: 82695 dataset_size: 176962 --- # Dataset Card for "mmlu-high_school_macroeconomics-verbal-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_lmsys__vicuna-13b-v1.5
--- pretty_name: Evaluation run of lmsys/vicuna-13b-v1.5 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lmsys/vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lmsys__vicuna-13b-v1.5\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-15T01:22:33.237446](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-13b-v1.5/blob/main/results_2023-10-15T01-22-33.237446.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.21403104026845637,\n\ \ \"em_stderr\": 0.004200304057589016,\n \"f1\": 0.2773447986577177,\n\ \ \"f1_stderr\": 0.004194161726605588,\n \"acc\": 0.4298049932592257,\n\ \ \"acc_stderr\": 0.010471546731533343\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.21403104026845637,\n \"em_stderr\": 0.004200304057589016,\n\ \ \"f1\": 0.2773447986577177,\n \"f1_stderr\": 0.004194161726605588\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11296436694465505,\n \ \ \"acc_stderr\": 0.008719339028833057\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.01222375443423363\n\ \ }\n}\n```" repo_url: https://huggingface.co/lmsys/vicuna-13b-v1.5 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|arc:challenge|25_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-09T10:24:27.985087.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_15T01_22_33.237446 path: - '**/details_harness|drop|3_2023-10-15T01-22-33.237446.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-15T01-22-33.237446.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_15T01_22_33.237446 path: - '**/details_harness|gsm8k|5_2023-10-15T01-22-33.237446.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-15T01-22-33.237446.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hellaswag|10_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:24:27.985087.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:24:27.985087.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T10_24_27.985087 path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T10:24:27.985087.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T10:24:27.985087.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_15T01_22_33.237446 path: - '**/details_harness|winogrande|5_2023-10-15T01-22-33.237446.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-15T01-22-33.237446.parquet' - config_name: results data_files: - split: 2023_08_09T10_24_27.985087 path: - results_2023-08-09T10:24:27.985087.parquet - split: 2023_10_15T01_22_33.237446 path: - results_2023-10-15T01-22-33.237446.parquet - split: latest path: - results_2023-10-15T01-22-33.237446.parquet --- # Dataset Card for Evaluation run of lmsys/vicuna-13b-v1.5 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lmsys/vicuna-13b-v1.5 - **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 [lmsys/vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lmsys__vicuna-13b-v1.5", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-15T01:22:33.237446](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-13b-v1.5/blob/main/results_2023-10-15T01-22-33.237446.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.21403104026845637, "em_stderr": 0.004200304057589016, "f1": 0.2773447986577177, "f1_stderr": 0.004194161726605588, "acc": 0.4298049932592257, "acc_stderr": 0.010471546731533343 }, "harness|drop|3": { "em": 0.21403104026845637, "em_stderr": 0.004200304057589016, "f1": 0.2773447986577177, "f1_stderr": 0.004194161726605588 }, "harness|gsm8k|5": { "acc": 0.11296436694465505, "acc_stderr": 0.008719339028833057 }, "harness|winogrande|5": { "acc": 0.7466456195737964, "acc_stderr": 0.01222375443423363 } } ``` ### 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]
316usman/thematic4d-pw-embed-part1
--- dataset_info: features: - name: text dtype: string - name: country dtype: string - name: document_url dtype: string - name: source_url dtype: string - name: num_tokens dtype: int64 splits: - name: train num_bytes: 392006393 num_examples: 616322 download_size: 152171067 dataset_size: 392006393 configs: - config_name: default data_files: - split: train path: data/train-* ---
anwarshome/RT_temp
--- dataset_info: features: - name: uuid dtype: string - name: sentence dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 58112550.0 num_examples: 686 - name: test num_bytes: 740753.0 num_examples: 10 download_size: 45644402 dataset_size: 58853303.0 --- # Dataset Card for "RT_temp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jjz5463/probing_dataset_5.0
--- size_categories: - n<1K dataset_info: features: - name: attributes struct: - name: length dtype: string - name: point_of_view dtype: string - name: sentence_type dtype: string - name: tense dtype: string - name: topic dtype: string - name: voice dtype: string - name: positive dtype: string - name: negative dtype: string - name: feature dtype: string splits: - name: train num_bytes: 122900 num_examples: 400 download_size: 49949 dataset_size: 122900 configs: - config_name: default data_files: - split: train path: data/train-* library_name: datadreamer tags: - datadreamer - datadreamer-0.25.0 - synthetic - gpt-4 --- # Dataset Card [Add more information here](https://huggingface.co/datasets/templates/dataset-card-example) --- This dataset was produced with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card can be found [here](datadreamer.json).
fedora-copr/pep-sum
--- language: - en multilinguality: - monolingual size_categories: - n<1K task_categories: - summarization - text-classification dataset_info: features: - name: text dtype: string - name: status dtype: string - name: title dtype: string - name: type dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4816611 num_examples: 345 download_size: 2525116 dataset_size: 4816611 configs: - config_name: default data_files: - split: train path: data/train-* ---
deadbits/vigil-instruction-bypass-all-MiniLM-L6-v2
--- tags: - embeddings - text - security pretty_name: 'Vigil: LLM Instruction Bypass all-MiniLM-L6-v2 ' --- # Vigil: LLM Instruction Bypass all-MiniLM-L6-v2 - **Repo:** [github.com/deadbits/vigil-llm](https://github.com/deadbits/vigil-llm) `Vigil` is a Python framework and REST API for assessing Large Language Model (LLM) prompts against a set of scanners to detect prompt injections, jailbreaks, and other potentially risky inputs. This repository contains `all-MiniLM-L6-v2` embeddings for all Instruction Bypass style prompts ("Ignore instructions ...") used by [Vigil](https://github.com/deadbits/prompt-injection-defense). You can use the [parquet2vdb.py](https://github.com/deadbits/prompt-injection-defense/blob/main/vigil/utils/parquet2vdb.py) utility to load the embeddings in the Vigil chromadb instance, or use them in your own application. ## Format ```json [ { "text": str, "embedding": [], "model": "all-MiniLM-L6-v2" } ] ``` Instruction bypass prompts generated with: https://gist.github.com/deadbits/e93a90aa36c9aa7b5ce1179597a6fe3d#file-generate-phrases-py
autoevaluate/autoeval-staging-eval-project-squad_v2-82949658-14045923
--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: Aiyshwariya/bert-finetuned-squad metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Aiyshwariya/bert-finetuned-squad * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v19_R8-7B
--- pretty_name: Evaluation run of PeanutJar/LLaMa-2-PeanutButter_v19_R8-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [PeanutJar/LLaMa-2-PeanutButter_v19_R8-7B](https://huggingface.co/PeanutJar/LLaMa-2-PeanutButter_v19_R8-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 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 agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v19_R8-7B\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-12T09:58:38.972064](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v19_R8-7B/blob/main/results_2023-09-12T09-58-38.972064.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\": 0.4674367304116677,\n\ \ \"acc_stderr\": 0.035284344124032196,\n \"acc_norm\": 0.4714260290393888,\n\ \ \"acc_norm_stderr\": 0.03526985338617593,\n \"mc1\": 0.2631578947368421,\n\ \ \"mc1_stderr\": 0.015415241740237014,\n \"mc2\": 0.3961362396399567,\n\ \ \"mc2_stderr\": 0.013785031017759436\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4948805460750853,\n \"acc_stderr\": 0.014610624890309157,\n\ \ \"acc_norm\": 0.5332764505119454,\n \"acc_norm_stderr\": 0.014578995859605802\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5902210714997013,\n\ \ \"acc_stderr\": 0.004907877144720015,\n \"acc_norm\": 0.7871937860983867,\n\ \ \"acc_norm_stderr\": 0.004084552641903664\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n\ \ \"acc_stderr\": 0.04313531696750575,\n \"acc_norm\": 0.4740740740740741,\n\ \ \"acc_norm_stderr\": 0.04313531696750575\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.40789473684210525,\n \"acc_stderr\": 0.03999309712777471,\n\ \ \"acc_norm\": 0.40789473684210525,\n \"acc_norm_stderr\": 0.03999309712777471\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.4377358490566038,\n \"acc_stderr\": 0.030533338430467516,\n\ \ \"acc_norm\": 0.4377358490566038,\n \"acc_norm_stderr\": 0.030533338430467516\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4583333333333333,\n\ \ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.4583333333333333,\n\ \ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.43352601156069365,\n\ \ \"acc_stderr\": 0.03778621079092055,\n \"acc_norm\": 0.43352601156069365,\n\ \ \"acc_norm_stderr\": 0.03778621079092055\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179964,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179964\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n\ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.425531914893617,\n \"acc_stderr\": 0.03232146916224469,\n\ \ \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.03232146916224469\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n\ \ \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n\ \ \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.04122737111370331,\n\ \ \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.04122737111370331\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2724867724867725,\n \"acc_stderr\": 0.022930973071633363,\n \"\ acc_norm\": 0.2724867724867725,\n \"acc_norm_stderr\": 0.022930973071633363\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3253968253968254,\n\ \ \"acc_stderr\": 0.04190596438871136,\n \"acc_norm\": 0.3253968253968254,\n\ \ \"acc_norm_stderr\": 0.04190596438871136\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5064516129032258,\n\ \ \"acc_stderr\": 0.02844163823354051,\n \"acc_norm\": 0.5064516129032258,\n\ \ \"acc_norm_stderr\": 0.02844163823354051\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.33497536945812806,\n \"acc_stderr\": 0.033208527423483104,\n\ \ \"acc_norm\": 0.33497536945812806,\n \"acc_norm_stderr\": 0.033208527423483104\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\"\ : {\n \"acc\": 0.5818181818181818,\n \"acc_stderr\": 0.03851716319398393,\n\ \ \"acc_norm\": 0.5818181818181818,\n \"acc_norm_stderr\": 0.03851716319398393\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.5151515151515151,\n \"acc_stderr\": 0.03560716516531061,\n \"\ acc_norm\": 0.5151515151515151,\n \"acc_norm_stderr\": 0.03560716516531061\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.6580310880829016,\n \"acc_stderr\": 0.03423465100104283,\n\ \ \"acc_norm\": 0.6580310880829016,\n \"acc_norm_stderr\": 0.03423465100104283\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.44358974358974357,\n \"acc_stderr\": 0.025189149894764198,\n\ \ \"acc_norm\": 0.44358974358974357,\n \"acc_norm_stderr\": 0.025189149894764198\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945277,\n \ \ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945277\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.031968769891957786,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.031968769891957786\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.03734535676787198,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.03734535676787198\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6330275229357798,\n \"acc_stderr\": 0.020664675659520525,\n \"\ acc_norm\": 0.6330275229357798,\n \"acc_norm_stderr\": 0.020664675659520525\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.26851851851851855,\n \"acc_stderr\": 0.030225226160012383,\n \"\ acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.030225226160012383\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5735294117647058,\n \"acc_stderr\": 0.03471157907953427,\n \"\ acc_norm\": 0.5735294117647058,\n \"acc_norm_stderr\": 0.03471157907953427\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6244725738396625,\n \"acc_stderr\": 0.03152256243091156,\n \ \ \"acc_norm\": 0.6244725738396625,\n \"acc_norm_stderr\": 0.03152256243091156\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5605381165919282,\n\ \ \"acc_stderr\": 0.03331092511038179,\n \"acc_norm\": 0.5605381165919282,\n\ \ \"acc_norm_stderr\": 0.03331092511038179\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5725190839694656,\n \"acc_stderr\": 0.04338920305792401,\n\ \ \"acc_norm\": 0.5725190839694656,\n \"acc_norm_stderr\": 0.04338920305792401\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6115702479338843,\n \"acc_stderr\": 0.04449270350068383,\n \"\ acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.04449270350068383\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5185185185185185,\n\ \ \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.5185185185185185,\n\ \ \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5214723926380368,\n \"acc_stderr\": 0.03924746876751129,\n\ \ \"acc_norm\": 0.5214723926380368,\n \"acc_norm_stderr\": 0.03924746876751129\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\ \ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\ \ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.5631067961165048,\n \"acc_stderr\": 0.049111471073657764,\n\ \ \"acc_norm\": 0.5631067961165048,\n \"acc_norm_stderr\": 0.049111471073657764\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6923076923076923,\n\ \ \"acc_stderr\": 0.03023638994217308,\n \"acc_norm\": 0.6923076923076923,\n\ \ \"acc_norm_stderr\": 0.03023638994217308\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6462324393358876,\n\ \ \"acc_stderr\": 0.017098184708161903,\n \"acc_norm\": 0.6462324393358876,\n\ \ \"acc_norm_stderr\": 0.017098184708161903\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.026919095102908273,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.026919095102908273\n \ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24022346368715083,\n\ \ \"acc_stderr\": 0.014288343803925293,\n \"acc_norm\": 0.24022346368715083,\n\ \ \"acc_norm_stderr\": 0.014288343803925293\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.477124183006536,\n \"acc_stderr\": 0.028599936776089775,\n\ \ \"acc_norm\": 0.477124183006536,\n \"acc_norm_stderr\": 0.028599936776089775\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5884244372990354,\n\ \ \"acc_stderr\": 0.02795048149440127,\n \"acc_norm\": 0.5884244372990354,\n\ \ \"acc_norm_stderr\": 0.02795048149440127\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.4845679012345679,\n \"acc_stderr\": 0.0278074900442762,\n\ \ \"acc_norm\": 0.4845679012345679,\n \"acc_norm_stderr\": 0.0278074900442762\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3829787234042553,\n \"acc_stderr\": 0.02899908090480618,\n \ \ \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.02899908090480618\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.363754889178618,\n\ \ \"acc_stderr\": 0.012286991879902884,\n \"acc_norm\": 0.363754889178618,\n\ \ \"acc_norm_stderr\": 0.012286991879902884\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5330882352941176,\n \"acc_stderr\": 0.03030625772246832,\n\ \ \"acc_norm\": 0.5330882352941176,\n \"acc_norm_stderr\": 0.03030625772246832\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.46078431372549017,\n \"acc_stderr\": 0.020165523313907904,\n \ \ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.020165523313907904\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5454545454545454,\n\ \ \"acc_stderr\": 0.04769300568972743,\n \"acc_norm\": 0.5454545454545454,\n\ \ \"acc_norm_stderr\": 0.04769300568972743\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5061224489795918,\n \"acc_stderr\": 0.03200682020163908,\n\ \ \"acc_norm\": 0.5061224489795918,\n \"acc_norm_stderr\": 0.03200682020163908\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6318407960199005,\n\ \ \"acc_stderr\": 0.03410410565495301,\n \"acc_norm\": 0.6318407960199005,\n\ \ \"acc_norm_stderr\": 0.03410410565495301\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.40963855421686746,\n\ \ \"acc_stderr\": 0.03828401115079022,\n \"acc_norm\": 0.40963855421686746,\n\ \ \"acc_norm_stderr\": 0.03828401115079022\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7017543859649122,\n \"acc_stderr\": 0.03508771929824563,\n\ \ \"acc_norm\": 0.7017543859649122,\n \"acc_norm_stderr\": 0.03508771929824563\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2631578947368421,\n\ \ \"mc1_stderr\": 0.015415241740237014,\n \"mc2\": 0.3961362396399567,\n\ \ \"mc2_stderr\": 0.013785031017759436\n }\n}\n```" repo_url: https://huggingface.co/PeanutJar/LLaMa-2-PeanutButter_v19_R8-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|arc:challenge|25_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hellaswag|10_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T09-58-38.972064.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T09-58-38.972064.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T09_58_38.972064 path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T09-58-38.972064.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T09-58-38.972064.parquet' - config_name: results data_files: - split: 2023_09_12T09_58_38.972064 path: - results_2023-09-12T09-58-38.972064.parquet - split: latest path: - results_2023-09-12T09-58-38.972064.parquet --- # Dataset Card for Evaluation run of PeanutJar/LLaMa-2-PeanutButter_v19_R8-7B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/PeanutJar/LLaMa-2-PeanutButter_v19_R8-7B - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [PeanutJar/LLaMa-2-PeanutButter_v19_R8-7B](https://huggingface.co/PeanutJar/LLaMa-2-PeanutButter_v19_R8-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v19_R8-7B", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-12T09:58:38.972064](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v19_R8-7B/blob/main/results_2023-09-12T09-58-38.972064.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": 0.4674367304116677, "acc_stderr": 0.035284344124032196, "acc_norm": 0.4714260290393888, "acc_norm_stderr": 0.03526985338617593, "mc1": 0.2631578947368421, "mc1_stderr": 0.015415241740237014, "mc2": 0.3961362396399567, "mc2_stderr": 0.013785031017759436 }, "harness|arc:challenge|25": { "acc": 0.4948805460750853, "acc_stderr": 0.014610624890309157, "acc_norm": 0.5332764505119454, "acc_norm_stderr": 0.014578995859605802 }, "harness|hellaswag|10": { "acc": 0.5902210714997013, "acc_stderr": 0.004907877144720015, "acc_norm": 0.7871937860983867, "acc_norm_stderr": 0.004084552641903664 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4740740740740741, "acc_stderr": 0.04313531696750575, "acc_norm": 0.4740740740740741, "acc_norm_stderr": 0.04313531696750575 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.40789473684210525, "acc_stderr": 0.03999309712777471, "acc_norm": 0.40789473684210525, "acc_norm_stderr": 0.03999309712777471 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4377358490566038, "acc_stderr": 0.030533338430467516, "acc_norm": 0.4377358490566038, "acc_norm_stderr": 0.030533338430467516 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4583333333333333, "acc_stderr": 0.04166666666666665, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.04166666666666665 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.34, "acc_stderr": 0.047609522856952344, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952344 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.43352601156069365, "acc_stderr": 0.03778621079092055, "acc_norm": 0.43352601156069365, "acc_norm_stderr": 0.03778621079092055 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179964, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179964 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.425531914893617, "acc_stderr": 0.03232146916224469, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.03232146916224469 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537315, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537315 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.42758620689655175, "acc_stderr": 0.04122737111370331, "acc_norm": 0.42758620689655175, "acc_norm_stderr": 0.04122737111370331 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2724867724867725, "acc_stderr": 0.022930973071633363, "acc_norm": 0.2724867724867725, "acc_norm_stderr": 0.022930973071633363 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3253968253968254, "acc_stderr": 0.04190596438871136, "acc_norm": 0.3253968253968254, "acc_norm_stderr": 0.04190596438871136 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5064516129032258, "acc_stderr": 0.02844163823354051, "acc_norm": 0.5064516129032258, "acc_norm_stderr": 0.02844163823354051 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.33497536945812806, "acc_stderr": 0.033208527423483104, "acc_norm": 0.33497536945812806, "acc_norm_stderr": 0.033208527423483104 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5818181818181818, "acc_stderr": 0.03851716319398393, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.03851716319398393 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5151515151515151, "acc_stderr": 0.03560716516531061, "acc_norm": 0.5151515151515151, "acc_norm_stderr": 0.03560716516531061 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6580310880829016, "acc_stderr": 0.03423465100104283, "acc_norm": 0.6580310880829016, "acc_norm_stderr": 0.03423465100104283 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.44358974358974357, "acc_stderr": 0.025189149894764198, "acc_norm": 0.44358974358974357, "acc_norm_stderr": 0.025189149894764198 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2814814814814815, "acc_stderr": 0.027420019350945277, "acc_norm": 0.2814814814814815, "acc_norm_stderr": 0.027420019350945277 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.031968769891957786, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.031968769891957786 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2980132450331126, "acc_stderr": 0.03734535676787198, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.03734535676787198 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6330275229357798, "acc_stderr": 0.020664675659520525, "acc_norm": 0.6330275229357798, "acc_norm_stderr": 0.020664675659520525 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.26851851851851855, "acc_stderr": 0.030225226160012383, "acc_norm": 0.26851851851851855, "acc_norm_stderr": 0.030225226160012383 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5735294117647058, "acc_stderr": 0.03471157907953427, "acc_norm": 0.5735294117647058, "acc_norm_stderr": 0.03471157907953427 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6244725738396625, "acc_stderr": 0.03152256243091156, "acc_norm": 0.6244725738396625, "acc_norm_stderr": 0.03152256243091156 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5605381165919282, "acc_stderr": 0.03331092511038179, "acc_norm": 0.5605381165919282, "acc_norm_stderr": 0.03331092511038179 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5725190839694656, "acc_stderr": 0.04338920305792401, "acc_norm": 0.5725190839694656, "acc_norm_stderr": 0.04338920305792401 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6115702479338843, "acc_stderr": 0.04449270350068383, "acc_norm": 0.6115702479338843, "acc_norm_stderr": 0.04449270350068383 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5185185185185185, "acc_stderr": 0.04830366024635331, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.04830366024635331 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5214723926380368, "acc_stderr": 0.03924746876751129, "acc_norm": 0.5214723926380368, "acc_norm_stderr": 0.03924746876751129 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness|hendrycksTest-management|5": { "acc": 0.5631067961165048, "acc_stderr": 0.049111471073657764, "acc_norm": 0.5631067961165048, "acc_norm_stderr": 0.049111471073657764 }, "harness|hendrycksTest-marketing|5": { "acc": 0.6923076923076923, "acc_stderr": 0.03023638994217308, "acc_norm": 0.6923076923076923, "acc_norm_stderr": 0.03023638994217308 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6462324393358876, "acc_stderr": 0.017098184708161903, "acc_norm": 0.6462324393358876, "acc_norm_stderr": 0.017098184708161903 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5, "acc_stderr": 0.026919095102908273, "acc_norm": 0.5, "acc_norm_stderr": 0.026919095102908273 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24022346368715083, "acc_stderr": 0.014288343803925293, "acc_norm": 0.24022346368715083, "acc_norm_stderr": 0.014288343803925293 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.477124183006536, "acc_stderr": 0.028599936776089775, "acc_norm": 0.477124183006536, "acc_norm_stderr": 0.028599936776089775 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5884244372990354, "acc_stderr": 0.02795048149440127, "acc_norm": 0.5884244372990354, "acc_norm_stderr": 0.02795048149440127 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4845679012345679, "acc_stderr": 0.0278074900442762, "acc_norm": 0.4845679012345679, "acc_norm_stderr": 0.0278074900442762 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3829787234042553, "acc_stderr": 0.02899908090480618, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.02899908090480618 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.363754889178618, "acc_stderr": 0.012286991879902884, "acc_norm": 0.363754889178618, "acc_norm_stderr": 0.012286991879902884 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5330882352941176, "acc_stderr": 0.03030625772246832, "acc_norm": 0.5330882352941176, "acc_norm_stderr": 0.03030625772246832 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.46078431372549017, "acc_stderr": 0.020165523313907904, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.020165523313907904 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5454545454545454, "acc_stderr": 0.04769300568972743, "acc_norm": 0.5454545454545454, "acc_norm_stderr": 0.04769300568972743 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5061224489795918, "acc_stderr": 0.03200682020163908, "acc_norm": 0.5061224489795918, "acc_norm_stderr": 0.03200682020163908 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6318407960199005, "acc_stderr": 0.03410410565495301, "acc_norm": 0.6318407960199005, "acc_norm_stderr": 0.03410410565495301 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-virology|5": { "acc": 0.40963855421686746, "acc_stderr": 0.03828401115079022, "acc_norm": 0.40963855421686746, "acc_norm_stderr": 0.03828401115079022 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7017543859649122, "acc_stderr": 0.03508771929824563, "acc_norm": 0.7017543859649122, "acc_norm_stderr": 0.03508771929824563 }, "harness|truthfulqa:mc|0": { "mc1": 0.2631578947368421, "mc1_stderr": 0.015415241740237014, "mc2": 0.3961362396399567, "mc2_stderr": 0.013785031017759436 } } ``` ### 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]
xNoper/dubai-aerial
--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 33524376.0 num_examples: 72 download_size: 32535970 dataset_size: 33524376.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
robinhad/databricks-dolly-15k-uk
--- license: cc-by-sa-3.0 task_categories: - question-answering - summarization language: - uk size_categories: - 10K<n<100K --- # Summary `databricks-dolly-15k-uk` is an open source dataset based on [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) instruction-following dataset, but machine translated using [facebook/m2m100_1.2B](https://huggingface.co/facebook/m2m100_1.2B) model. Tasks covered include brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. Expect this dataset to not be grammatically correct and having obvious pitfalls of machine translation. <details> <summary>Original Summary</summary> # Summary `databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: Ukrainian Version: 1.0 **Owner: Databricks, Inc.** # Dataset Overview `databricks-dolly-15k` is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category. Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly. For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the `context` field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. `[42]`) which we recommend users remove for downstream applications. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories. Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets. # Dataset ## Purpose of Collection As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - **Human-generated data**: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories. - **Wikipedia**: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages. ## Annotator Guidelines To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor. The annotation guidelines for each of the categories are as follows: - **Creative Writing**: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better. - **Closed QA**: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form. - **Open QA**: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation. - **Summarization**: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form. - **Information Extraction**: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form. - **Classification**: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better. - **Brainstorming**: Think up lots of examples in response to a question asking to brainstorm ideas. ## Personal or Sensitive Data This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language American English # Known Limitations - Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia - Some annotators may not be native English speakers - Annotator demographics and subject matter may reflect the makeup of Databricks employees # License/Attribution **Copyright (2023) Databricks, Inc.** This dataset was developed at Databricks (https://www.databricks.com) and its use is subject to the CC BY-SA 3.0 license. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license: Wikipedia (various pages) - https://www.wikipedia.org/ Copyright © Wikipedia editors and contributors. </details>
xufana/RedPajama-INCITE-Instruct-3B-Addition
--- license: apache-2.0 task_categories: - text-generation language: - en tags: - arithmetics pretty_name: RedPajama Arithmetics size_categories: - 100K<n<1M --- ## Dataset Summary The Arithmetic Operations Dataset is a synteticly generated collection of mathematical arithmetic operations for practice and evaluation purposes. It contains a total of 624,800 arithmetic operations, consisting of 568,000 addition operations and 56,800 subtraction operations. The dataset is designed to provide a range of arithmetic problems to train and evaluate language models for solving simple arithmetic (mostly addition, the others TBA) problems. ## Dataset Structure The dataset is organized into two main categories: addition and subtraction. Each category contains a set of arithmetic operations in separate files (`addition.json`) and (`subtraction.json`), and the file (`dataset.json`) provides combined data from both. ### Data Instances ```bash { "instruction": "What is the answer to 373486002216116154 + 339369?", "input": "373486002216116154 + 339369", "output": "373486002216116154 + 339369 = 373486002216455523", "answer": "373486002216455523" }, { "instruction": "9916607491627649 minus 581954", "input": "9916607491627649 - 581954", "output": "9916607491627649 - 581954 = 9916607491045695", "answer": "9916607491045695" }, ``` ### Data Fields The files share the same structure and have 4 fields: - `instruction`: Human instructions are generated by inserting arithmetic expressions into randomly selected templates and incorporating natural language variations. These instructions are intended to serve as prompts for instruction-finetuning, providing input for training the model. - `input`: A randomly generated arithmetic expression, that can serve as a substitute for the 'instruction' component during training, allowing a specific focus on arithmetic operations while minimizing the impact of natural language. - `output`: the target output for the model to learn. - `answer`: direct numerical answer to the arithmetic task. It can be used to test learnability of various sub-tasks. ## Contact For any questions or inquiries regarding this dataset, please contact xufana@yandex.ru.
barunsaha/aya_dataset_ben_translated
--- license: apache-2.0 dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 11918662 num_examples: 6633 - name: test num_bytes: 308222 num_examples: 250 download_size: 4492541 dataset_size: 12226884 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* task_categories: - question-answering language: - bn pretty_name: (Subset of) Aya dataset translated to Bengali size_categories: - 1K<n<10K --- `aya_dataset_ben_translated` is a subset of the [aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), with some modifications. In particular, the original data points in Bengali (indicated by the `language` or `language_code` columns) are retained. In addition, the English and Hindi data points are translated into Bengali using Google Cloud Translation API. All columns from the original dataset are retained. A handful of inaccuracies arising out of translation have been fixed so far. Therefore, the dataset can be a bit noisy. This is particularly true for coding related questions and answers. Moreover, some non-Bengali characters can be found in the text. In addition, potential duplicates from the original dataset are retained as well.
Fhrozen/CABankSakura
--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - ja license: - cc multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - found task_categories: - audio-classification - automatic-speech-recognition task_ids: - speaker-identification pretty_name: banksakura tags: - speech-recognition --- # CABank Japanese Sakura Corpus - Susanne Miyata - Department of Medical Sciences - Aichi Shukotoku University - smiyata@asu.aasa.ac.jp - website: https://ca.talkbank.org/access/Sakura.html ## Important This data set is a copy from the original one located at https://ca.talkbank.org/access/Sakura.html. ## Details - Participants: 31 - Type of Study: xxx - Location: Japan - Media type: audio - DOI: doi:10.21415/T5M90R ## Citation information Some citation here. In accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references. ## Project Description This corpus of 18 conversations is the product of six graduation theses on gender differences in students' group talk. Each conversation lasted between 12 and 35 minutes (avg. 25 minutes) resulting in an overall time of 7 hours and 30 minutes. 31 Students (19 female, 12 male) participated in the study (Table 1). The participants gathered in groups of 4 students, either of the same or the opposite sex (6 conversations with a group of 4 female students, 6 with 4 male students, and 6 conversations with 2 male and 2 female students), according to age (first and third year students) and affiliation (two academic departments). In addition, the participants of each conversation came from the same small-sized class and were well acquainted. The participants were informed that their conversations may be transcribed and a video recorded for use in possible publication when recruited. Additionally, permission was asked once more after the transcription in cases where either private information had been displayed, or a misunderstanding concerning the nature and degree of the publication of the conversations became apparent during the conversation. The recordings took place in a small conference room at the university between or after lectures. The participants were given a card with a conversation topic to start with, but were free to vary (topic 1 "What do you expect from an opposite sex friend?" [isee ni motomeru koto]; topic 2 "Are you a dog lover or a cat lover?" [inuha ka nekoha ka]; topic 3 "About part-time work" [arubaito ni tsuite]). The investigator was not present during the recording. The combination of participants, the topic, and the duration of the 18 conversations are given in Table 2. The participants produced 15,449 utterances overall (female: 8,027 utterances, male: 7,422 utterances). All utterances were linked to video and transcribed in regular Japanese orthography and Latin script (Wakachi2002), and provided with morphological tags (JMOR04.1). Proper names were replaced by pseudonyms. ## Acknowledgements Additional contributors: Banno, Kyoko; Konishi, Saya; Matsui, Ayumi; Matsumoto, Shiori; Oogi, Rie; Takahashi, Akane; Muraki, Kyoko.
SpongeBash/hugging_face
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 75931.0 num_examples: 12 download_size: 77302 dataset_size: 75931.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "hugging_face" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
duckaiml/mc4_310
--- license: other dataset_info: config_name: ko features: - name: source dtype: string - name: id dtype: string - name: text dtype: string - name: added dtype: string - name: timestamp dtype: timestamp[s] - name: metadata struct: - name: url dtype: string - name: lang struct: - name: ko.tfrecord dtype: float64 splits: - name: train num_bytes: 151177516676 num_examples: 24035493 download_size: 16185376673 dataset_size: 151177516676 configs: - config_name: ko data_files: - split: train path: ko/train-* --- mc4 but in HPC friendly parquet format (32GiB shards) Attribution,license, copyright info: [Google](https://www.tensorflow.org/datasets/catalog/c4) and [AI^2](https://huggingface.co/datasets/allenai/c4) for producing and uploading them.