Datasets:
davanstrien
/
summary-test

Dataset card Data Studio Files
xet
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AmanK1202/LogoGeneration_png
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 120298419.0 num_examples: 821 download_size: 120174466 dataset_size: 120298419.0 --- # Dataset Card for "LogoGeneration_png" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-6fbfec76-7855041
--- type: predictions tags: - autotrain - evaluation datasets: - samsum eval_info: task: summarization model: knkarthick/bart-large-xsum-samsum metrics: [] dataset_name: samsum dataset_config: samsum dataset_split: test col_mapping: text: dialogue target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: knkarthick/bart-large-xsum-samsum * Dataset: samsum 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.
erwinqi/conslam_relabelled_semantic_reduced
--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 401239316.0 num_examples: 88 - name: validation num_bytes: 49226249.0 num_examples: 10 download_size: 450462129 dataset_size: 450465565.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
matvelen6369/eeg_data
--- dataset_info: features: - name: eeg sequence: float64 splits: - name: train num_bytes: 6066918000 num_examples: 49716 download_size: 4934969605 dataset_size: 6066918000 configs: - config_name: default data_files: - split: train path: data/train-* ---
tyzhu/squad_wrong_title_v4_train_30_eval_10_recite
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 681763 num_examples: 368 - name: validation num_bytes: 84048 num_examples: 50 download_size: 137622 dataset_size: 765811 --- # Dataset Card for "squad_wrong_title_v4_train_30_eval_10_recite" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ThWu/dpo_highest_n_random
--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 519912724 num_examples: 182470 download_size: 243211283 dataset_size: 519912724 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dpo_highest_n_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wangyi111/EuroSAT-SAR
--- license: mit task_categories: - image-classification --- ## EuroSAT-SAR: Land Use and Land Cover Classification with Sentinel-1 The EuroSAT-SAR dataset is a SAR version of the popular [EuroSAT](https://github.com/phelber/EuroSAT) dataset. We matched each Sentinel-2 image in EuroSAT with one Sentinel-1 patch according to the geospatial coordinates, ending up with 27,000 dual-pol Sentinel-1 SAR images divided in 10 classes. The EuroSAT-SAR dataset was collected as one downstream task in the work [FG-MAE](https://github.com/zhu-xlab/FGMAE) to serve as a CIFAR-like, clean, balanced ML-ready dataset for remote sensing SAR image recognition. <p align="center"> <img width="1000" alt="fgmae main structure" src="assets/eurosat-sar.png"> </p> The dataset can be downloaded as a compressed zip file [here](https://huggingface.co/datasets/wangyi111/EuroSAT-SAR/resolve/main/EuroSAT-SAR.zip). ### Citation ```bibtex @article{wang2023feature, title={Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing}, author={Wang, Yi and Hern{\'a}ndez, Hugo Hern{\'a}ndez and Albrecht, Conrad M and Zhu, Xiao Xiang}, journal={arXiv preprint arXiv:2310.18653}, year={2023} } ```
jiaqianjing/PatentData
--- license: gpl-3.0 task_categories: - text-generation language: - zh tags: - patent --- ## 数据来源 **[中国专利信息中心](https://patdata2.cnipa.gov.cn/)** ## 字段解释 * patent_id：专利编号 * patent_pub_date：专利公布日期 * title：专利名称 * applicant：申请人/单位 * application_date：申请日期 * inventors：发明人 * summary：摘要 * description：说明书全文 * claim：专利权利要求书全文 ## 使用限制 仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。 本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
autoevaluate/autoeval-eval-project-squad_v2-7b0e814c-1303349869
--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: nbroad/rob-base-superqa2 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: nbroad/rob-base-superqa2 * 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 [@nbroad](https://huggingface.co/nbroad) for evaluating this model.
loremipsum3658/sick-br
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: pair_ID dtype: int64 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: entailment_label dtype: string - name: relatedness_score dtype: float64 - name: entailment_AB dtype: string - name: entailment_BA dtype: string - name: sentence_A_original dtype: string - name: sentence_B_original dtype: string - name: sentence_A_dataset dtype: string - name: sentence_B_dataset dtype: string - name: SemEval_set dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 2196243 num_examples: 6887 - name: test num_bytes: 470001 num_examples: 1477 - name: validation num_bytes: 470022 num_examples: 1476 download_size: 1217241 dataset_size: 3136266 --- # Dataset Card for "sick-br" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nateraw/imagenet-sketch
--- license: mit ---
bigbio/mirna
--- language: - en bigbio_language: - English license: cc-by-nc-3.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_NC_3p0 pretty_name: miRNA homepage: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html bigbio_pubmed: True bigbio_public: True bigbio_tasks: - NAMED_ENTITY_RECOGNITION - NAMED_ENTITY_DISAMBIGUATION --- # Dataset Card for miRNA ## Dataset Description - **Homepage:** https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html - **Pubmed:** True - **Public:** True - **Tasks:** NER,NED The corpus consists of 301 Medline citations. The documents were screened for mentions of miRNA in the abstract text. Gene, disease and miRNA entities were manually annotated. The corpus comprises of two separate files, a train and a test set, coming from 201 and 100 documents respectively. ## Citation Information ``` @Article{Bagewadi2014, author={Bagewadi, Shweta and Bobi{'{c}}, Tamara and Hofmann-Apitius, Martin and Fluck, Juliane and Klinger, Roman}, title={Detecting miRNA Mentions and Relations in Biomedical Literature}, journal={F1000Research}, year={2014}, month={Aug}, day={28}, publisher={F1000Research}, volume={3}, pages={205-205}, keywords={MicroRNAs; corpus; prediction algorithms}, abstract={ INTRODUCTION: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. MOTIVATION: Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. RESULTS: The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an F1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. AVAILABILITY: The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html. }, note={26535109[pmid]}, note={PMC4602280[pmcid]}, issn={2046-1402}, url={https://pubmed.ncbi.nlm.nih.gov/26535109}, language={eng} } ```
bertbsb/prtimvoz
--- license: openrail ---
sabaaziz24/_
--- license: openrail ---
bdsaglam/musique-answerable-2hop-subset-erx-reward-2023-12-30T17-40-15
--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: reward dtype: int64 splits: - name: train num_bytes: 128205 num_examples: 90 download_size: 18415 dataset_size: 128205 configs: - config_name: default data_files: - split: train path: data/train-* ---
tyzhu/fw_num_bi_train_100_eval_100
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: train_doc2id path: data/train_doc2id-* - split: train_id2doc path: data/train_id2doc-* - split: train_find_word path: data/train_find_word-* - split: eval_find_word path: data/eval_find_word-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 36899 num_examples: 500 - name: train_doc2id num_bytes: 15892 num_examples: 200 - name: train_id2doc num_bytes: 16492 num_examples: 200 - name: train_find_word num_bytes: 4515 num_examples: 100 - name: eval_find_word num_bytes: 4623 num_examples: 100 download_size: 44830 dataset_size: 78421 --- # Dataset Card for "fw_num_bi_train_100_eval_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BByrneLab/EVQA_PreFLMR_preprocessed_passages
--- dataset_info: features: - name: language dtype: string - name: passage_id dtype: string - name: passage_content dtype: string splits: - name: train_passages num_bytes: 58570897 num_examples: 50205 - name: valid_passages num_bytes: 59117345 num_examples: 50753 - name: test_passages num_bytes: 60113716 num_examples: 51472 download_size: 106160568 dataset_size: 177801958 configs: - config_name: default data_files: - split: train_passages path: data/train_passages-* - split: valid_passages path: data/valid_passages-* - split: test_passages path: data/test_passages-* ---
yuvalkirstain/pexel_images_lots_with_generated_captions
--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: generated_caption dtype: string splits: - name: train num_bytes: 2467169489.125 num_examples: 7999 download_size: 2418777187 dataset_size: 2467169489.125 --- # Dataset Card for "pexel_images_lots_with_generated_captions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_HuggingFaceTB__cosmo-1b
--- pretty_name: Evaluation run of HuggingFaceTB/cosmo-1b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [HuggingFaceTB/cosmo-1b](https://huggingface.co/HuggingFaceTB/cosmo-1b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HuggingFaceTB__cosmo-1b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-20T22:24:29.025319](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceTB__cosmo-1b/blob/main/results_2024-02-20T22-24-29.025319.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\": 0.2698889533621004,\n\ \ \"acc_stderr\": 0.0314781880414406,\n \"acc_norm\": 0.2719343408831061,\n\ \ \"acc_norm_stderr\": 0.03221191811445044,\n \"mc1\": 0.2215422276621787,\n\ \ \"mc1_stderr\": 0.014537867601301137,\n \"mc2\": 0.38259377102490544,\n\ \ \"mc2_stderr\": 0.014283688892810937\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3302047781569966,\n \"acc_stderr\": 0.013743085603760427,\n\ \ \"acc_norm\": 0.3856655290102389,\n \"acc_norm_stderr\": 0.014224250973257174\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4182433778131846,\n\ \ \"acc_stderr\": 0.0049226246369452435,\n \"acc_norm\": 0.5507866958773153,\n\ \ \"acc_norm_stderr\": 0.004963974504003025\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.03785714465066654,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.03785714465066654\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.3223684210526316,\n \"acc_stderr\": 0.038035102483515854,\n\ \ \"acc_norm\": 0.3223684210526316,\n \"acc_norm_stderr\": 0.038035102483515854\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.33,\n\ \ \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \ \ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.28679245283018867,\n \"acc_stderr\": 0.027834912527544088,\n\ \ \"acc_norm\": 0.28679245283018867,\n \"acc_norm_stderr\": 0.027834912527544088\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\"\ : 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\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\": 0.28901734104046245,\n\ \ \"acc_stderr\": 0.03456425745087001,\n \"acc_norm\": 0.28901734104046245,\n\ \ \"acc_norm_stderr\": 0.03456425745087001\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929775,\n\ \ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929775\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.31063829787234043,\n \"acc_stderr\": 0.030251237579213167,\n\ \ \"acc_norm\": 0.31063829787234043,\n \"acc_norm_stderr\": 0.030251237579213167\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.16551724137931034,\n \"acc_stderr\": 0.03097055996622408,\n\ \ \"acc_norm\": 0.16551724137931034,\n \"acc_norm_stderr\": 0.03097055996622408\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1984126984126984,\n\ \ \"acc_stderr\": 0.035670166752768614,\n \"acc_norm\": 0.1984126984126984,\n\ \ \"acc_norm_stderr\": 0.035670166752768614\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.2161290322580645,\n\ \ \"acc_stderr\": 0.023415293433568532,\n \"acc_norm\": 0.2161290322580645,\n\ \ \"acc_norm_stderr\": 0.023415293433568532\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.23645320197044334,\n \"acc_stderr\": 0.029896114291733545,\n\ \ \"acc_norm\": 0.23645320197044334,\n \"acc_norm_stderr\": 0.029896114291733545\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.25252525252525254,\n \"acc_stderr\": 0.030954055470365897,\n \"\ acc_norm\": 0.25252525252525254,\n \"acc_norm_stderr\": 0.030954055470365897\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.20207253886010362,\n \"acc_stderr\": 0.02897908979429673,\n\ \ \"acc_norm\": 0.20207253886010362,\n \"acc_norm_stderr\": 0.02897908979429673\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2512820512820513,\n \"acc_stderr\": 0.02199201666237054,\n \ \ \"acc_norm\": 0.2512820512820513,\n \"acc_norm_stderr\": 0.02199201666237054\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2814814814814815,\n \"acc_stderr\": 0.02742001935094528,\n \ \ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.02742001935094528\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.02934457250063435,\n \ \ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.02934457250063435\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389023,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389023\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.22385321100917432,\n \"acc_stderr\": 0.017871217767790205,\n \"\ acc_norm\": 0.22385321100917432,\n \"acc_norm_stderr\": 0.017871217767790205\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.2962962962962963,\n \"acc_stderr\": 0.03114144782353603,\n \"\ acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.03114144782353603\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.27450980392156865,\n \"acc_stderr\": 0.03132179803083289,\n \"\ acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.03132179803083289\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.2911392405063291,\n \"acc_stderr\": 0.02957160106575337,\n \ \ \"acc_norm\": 0.2911392405063291,\n \"acc_norm_stderr\": 0.02957160106575337\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3183856502242152,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.3183856502242152,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.3140495867768595,\n \"acc_stderr\": 0.04236964753041017,\n \"\ acc_norm\": 0.3140495867768595,\n \"acc_norm_stderr\": 0.04236964753041017\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.18404907975460122,\n \"acc_stderr\": 0.030446777687971757,\n\ \ \"acc_norm\": 0.18404907975460122,\n \"acc_norm_stderr\": 0.030446777687971757\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.04059867246952686,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.04059867246952686\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.23300970873786409,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.23300970873786409,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.26495726495726496,\n\ \ \"acc_stderr\": 0.02891120880274946,\n \"acc_norm\": 0.26495726495726496,\n\ \ \"acc_norm_stderr\": 0.02891120880274946\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26436781609195403,\n\ \ \"acc_stderr\": 0.01576998484069052,\n \"acc_norm\": 0.26436781609195403,\n\ \ \"acc_norm_stderr\": 0.01576998484069052\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.023176298203992023,\n\ \ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.023176298203992023\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574882,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574882\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.02609016250427904,\n\ \ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.02609016250427904\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.26366559485530544,\n\ \ \"acc_stderr\": 0.025025538500532338,\n \"acc_norm\": 0.26366559485530544,\n\ \ \"acc_norm_stderr\": 0.025025538500532338\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.26851851851851855,\n \"acc_stderr\": 0.024659685185967267,\n\ \ \"acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.024659685185967267\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.24113475177304963,\n \"acc_stderr\": 0.025518731049537776,\n \ \ \"acc_norm\": 0.24113475177304963,\n \"acc_norm_stderr\": 0.025518731049537776\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2633637548891786,\n\ \ \"acc_stderr\": 0.0112495064036053,\n \"acc_norm\": 0.2633637548891786,\n\ \ \"acc_norm_stderr\": 0.0112495064036053\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.16911764705882354,\n \"acc_stderr\": 0.022770868010113014,\n\ \ \"acc_norm\": 0.16911764705882354,\n \"acc_norm_stderr\": 0.022770868010113014\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2434640522875817,\n \"acc_stderr\": 0.01736247376214663,\n \ \ \"acc_norm\": 0.2434640522875817,\n \"acc_norm_stderr\": 0.01736247376214663\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3090909090909091,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.3090909090909091,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.3224489795918367,\n \"acc_stderr\": 0.029923100563683906,\n\ \ \"acc_norm\": 0.3224489795918367,\n \"acc_norm_stderr\": 0.029923100563683906\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.21890547263681592,\n\ \ \"acc_stderr\": 0.029239174636647,\n \"acc_norm\": 0.21890547263681592,\n\ \ \"acc_norm_stderr\": 0.029239174636647\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2710843373493976,\n\ \ \"acc_stderr\": 0.034605799075530276,\n \"acc_norm\": 0.2710843373493976,\n\ \ \"acc_norm_stderr\": 0.034605799075530276\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.30409356725146197,\n \"acc_stderr\": 0.03528211258245231,\n\ \ \"acc_norm\": 0.30409356725146197,\n \"acc_norm_stderr\": 0.03528211258245231\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2215422276621787,\n\ \ \"mc1_stderr\": 0.014537867601301137,\n \"mc2\": 0.38259377102490544,\n\ \ \"mc2_stderr\": 0.014283688892810937\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.55327545382794,\n \"acc_stderr\": 0.013972488371616692\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.053828658074298714,\n \ \ \"acc_stderr\": 0.0062163286402381465\n }\n}\n```" repo_url: https://huggingface.co/HuggingFaceTB/cosmo-1b 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_20T22_17_27.049029 path: - '**/details_harness|arc:challenge|25_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|arc:challenge|25_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-20T22-24-29.025319.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|gsm8k|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|gsm8k|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hellaswag|10_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hellaswag|10_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-20T22-17-27.049029.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-20T22-24-29.025319.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-management|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-management|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T22-24-29.025319.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|truthfulqa:mc|0_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|truthfulqa:mc|0_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-20T22-24-29.025319.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_20T22_17_27.049029 path: - '**/details_harness|winogrande|5_2024-02-20T22-17-27.049029.parquet' - split: 2024_02_20T22_24_29.025319 path: - '**/details_harness|winogrande|5_2024-02-20T22-24-29.025319.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-20T22-24-29.025319.parquet' - config_name: results data_files: - split: 2024_02_20T22_17_27.049029 path: - results_2024-02-20T22-17-27.049029.parquet - split: 2024_02_20T22_24_29.025319 path: - results_2024-02-20T22-24-29.025319.parquet - split: latest path: - results_2024-02-20T22-24-29.025319.parquet --- # Dataset Card for Evaluation run of HuggingFaceTB/cosmo-1b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [HuggingFaceTB/cosmo-1b](https://huggingface.co/HuggingFaceTB/cosmo-1b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_HuggingFaceTB__cosmo-1b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-20T22:24:29.025319](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceTB__cosmo-1b/blob/main/results_2024-02-20T22-24-29.025319.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": 0.2698889533621004, "acc_stderr": 0.0314781880414406, "acc_norm": 0.2719343408831061, "acc_norm_stderr": 0.03221191811445044, "mc1": 0.2215422276621787, "mc1_stderr": 0.014537867601301137, "mc2": 0.38259377102490544, "mc2_stderr": 0.014283688892810937 }, "harness|arc:challenge|25": { "acc": 0.3302047781569966, "acc_stderr": 0.013743085603760427, "acc_norm": 0.3856655290102389, "acc_norm_stderr": 0.014224250973257174 }, "harness|hellaswag|10": { "acc": 0.4182433778131846, "acc_stderr": 0.0049226246369452435, "acc_norm": 0.5507866958773153, "acc_norm_stderr": 0.004963974504003025 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.25925925925925924, "acc_stderr": 0.03785714465066654, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.03785714465066654 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.3223684210526316, "acc_stderr": 0.038035102483515854, "acc_norm": 0.3223684210526316, "acc_norm_stderr": 0.038035102483515854 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.28679245283018867, "acc_stderr": 0.027834912527544088, "acc_norm": 0.28679245283018867, "acc_norm_stderr": 0.027834912527544088 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.25, "acc_stderr": 0.03621034121889507, "acc_norm": 0.25, "acc_norm_stderr": 0.03621034121889507 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "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": 0.28901734104046245, "acc_stderr": 0.03456425745087001, "acc_norm": 0.28901734104046245, "acc_norm_stderr": 0.03456425745087001 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.29411764705882354, "acc_stderr": 0.04533838195929775, "acc_norm": 0.29411764705882354, "acc_norm_stderr": 0.04533838195929775 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.31063829787234043, "acc_stderr": 0.030251237579213167, "acc_norm": 0.31063829787234043, "acc_norm_stderr": 0.030251237579213167 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813365, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813365 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.16551724137931034, "acc_stderr": 0.03097055996622408, "acc_norm": 0.16551724137931034, "acc_norm_stderr": 0.03097055996622408 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.1984126984126984, "acc_stderr": 0.035670166752768614, "acc_norm": 0.1984126984126984, "acc_norm_stderr": 0.035670166752768614 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.2, "acc_stderr": 0.04020151261036846, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036846 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.2161290322580645, "acc_stderr": 0.023415293433568532, "acc_norm": 0.2161290322580645, "acc_norm_stderr": 0.023415293433568532 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.23645320197044334, "acc_stderr": 0.029896114291733545, "acc_norm": 0.23645320197044334, "acc_norm_stderr": 0.029896114291733545 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.24242424242424243, "acc_stderr": 0.03346409881055953, "acc_norm": 0.24242424242424243, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.25252525252525254, "acc_stderr": 0.030954055470365897, "acc_norm": 0.25252525252525254, "acc_norm_stderr": 0.030954055470365897 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.20207253886010362, "acc_stderr": 0.02897908979429673, "acc_norm": 0.20207253886010362, "acc_norm_stderr": 0.02897908979429673 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2512820512820513, "acc_stderr": 0.02199201666237054, "acc_norm": 0.2512820512820513, "acc_norm_stderr": 0.02199201666237054 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2814814814814815, "acc_stderr": 0.02742001935094528, "acc_norm": 0.2814814814814815, "acc_norm_stderr": 0.02742001935094528 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.2857142857142857, "acc_stderr": 0.02934457250063435, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.02934457250063435 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389023, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389023 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.22385321100917432, "acc_stderr": 0.017871217767790205, "acc_norm": 0.22385321100917432, "acc_norm_stderr": 0.017871217767790205 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.03114144782353603, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.03114144782353603 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.27450980392156865, "acc_stderr": 0.03132179803083289, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.03132179803083289 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2911392405063291, "acc_stderr": 0.02957160106575337, "acc_norm": 0.2911392405063291, "acc_norm_stderr": 0.02957160106575337 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.3183856502242152, "acc_stderr": 0.03126580522513713, "acc_norm": 0.3183856502242152, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.3140495867768595, "acc_stderr": 0.04236964753041017, "acc_norm": 0.3140495867768595, "acc_norm_stderr": 0.04236964753041017 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.25, "acc_stderr": 0.04186091791394607, "acc_norm": 0.25, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.18404907975460122, "acc_stderr": 0.030446777687971757, "acc_norm": 0.18404907975460122, "acc_norm_stderr": 0.030446777687971757 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.24107142857142858, "acc_stderr": 0.04059867246952686, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.04059867246952686 }, "harness|hendrycksTest-management|5": { "acc": 0.23300970873786409, "acc_stderr": 0.04185832598928315, "acc_norm": 0.23300970873786409, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.26495726495726496, "acc_stderr": 0.02891120880274946, "acc_norm": 0.26495726495726496, "acc_norm_stderr": 0.02891120880274946 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.26436781609195403, "acc_stderr": 0.01576998484069052, "acc_norm": 0.26436781609195403, "acc_norm_stderr": 0.01576998484069052 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24566473988439305, "acc_stderr": 0.023176298203992023, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.023176298203992023 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574882, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574882 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.29411764705882354, "acc_stderr": 0.02609016250427904, "acc_norm": 0.29411764705882354, "acc_norm_stderr": 0.02609016250427904 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.26366559485530544, "acc_stderr": 0.025025538500532338, "acc_norm": 0.26366559485530544, "acc_norm_stderr": 0.025025538500532338 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.26851851851851855, "acc_stderr": 0.024659685185967267, "acc_norm": 0.26851851851851855, "acc_norm_stderr": 0.024659685185967267 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.24113475177304963, "acc_stderr": 0.025518731049537776, "acc_norm": 0.24113475177304963, "acc_norm_stderr": 0.025518731049537776 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2633637548891786, "acc_stderr": 0.0112495064036053, "acc_norm": 0.2633637548891786, "acc_norm_stderr": 0.0112495064036053 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.16911764705882354, "acc_stderr": 0.022770868010113014, "acc_norm": 0.16911764705882354, "acc_norm_stderr": 0.022770868010113014 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2434640522875817, "acc_stderr": 0.01736247376214663, "acc_norm": 0.2434640522875817, "acc_norm_stderr": 0.01736247376214663 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.3090909090909091, "acc_stderr": 0.044262946482000985, "acc_norm": 0.3090909090909091, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.3224489795918367, "acc_stderr": 0.029923100563683906, "acc_norm": 0.3224489795918367, "acc_norm_stderr": 0.029923100563683906 }, "harness|hendrycksTest-sociology|5": { "acc": 0.21890547263681592, "acc_stderr": 0.029239174636647, "acc_norm": 0.21890547263681592, "acc_norm_stderr": 0.029239174636647 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-virology|5": { "acc": 0.2710843373493976, "acc_stderr": 0.034605799075530276, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.034605799075530276 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.30409356725146197, "acc_stderr": 0.03528211258245231, "acc_norm": 0.30409356725146197, "acc_norm_stderr": 0.03528211258245231 }, "harness|truthfulqa:mc|0": { "mc1": 0.2215422276621787, "mc1_stderr": 0.014537867601301137, "mc2": 0.38259377102490544, "mc2_stderr": 0.014283688892810937 }, "harness|winogrande|5": { "acc": 0.55327545382794, "acc_stderr": 0.013972488371616692 }, "harness|gsm8k|5": { "acc": 0.053828658074298714, "acc_stderr": 0.0062163286402381465 } } ``` ## 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]
yuvalkirstain/PickaPic-rankings-6-3-2023
--- dataset_info: features: - name: ranking_id dtype: int64 - name: created_at dtype: timestamp[ns] - name: user_id dtype: int64 - name: image_1_uid dtype: string - name: image_2_uid dtype: string - name: image_3_uid dtype: 'null' - name: image_4_uid dtype: 'null' - name: best_image_uid dtype: string - name: prompt dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 16312020 num_examples: 71457 download_size: 5911046 dataset_size: 16312020 --- # Dataset Card for "PickaPic-rankings-6-3-2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kadialkad/snli-s
--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: test num_bytes: 125501 num_examples: 1000 - name: train num_bytes: 5994668 num_examples: 50000 - name: validation num_bytes: 128470 num_examples: 1000 download_size: 1870273 dataset_size: 6248639 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* ---
strombergnlp/pypi-20211209
--- license: apache-2.0 ---
liujqian/commonsenseqa_with_content_words
--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: question_concept dtype: string - name: choices sequence: - name: label dtype: string - name: text dtype: string - name: answerKey dtype: string - name: question_content_words sequence: string - name: choice_0_content_words sequence: string - name: choice_1_content_words sequence: string - name: choice_2_content_words sequence: string - name: choice_3_content_words sequence: string - name: choice_4_content_words sequence: string splits: - name: train num_bytes: 3595329 num_examples: 9741 - name: validation num_bytes: 446090 num_examples: 1221 - name: test num_bytes: 419929 num_examples: 1140 download_size: 2361458 dataset_size: 4461348 --- # Dataset Card for "commonsenseqa_with_content_words" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_CalderaAI__13B-Thorns-l2
--- pretty_name: Evaluation run of CalderaAI/13B-Thorns-l2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CalderaAI/13B-Thorns-l2](https://huggingface.co/CalderaAI/13B-Thorns-l2) 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_CalderaAI__13B-Thorns-l2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-24T07:53:37.765793](https://huggingface.co/datasets/open-llm-leaderboard/details_CalderaAI__13B-Thorns-l2/blob/main/results_2023-10-24T07-53-37.765793.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\": 0.38873741610738255,\n\ \ \"em_stderr\": 0.004992082219869444,\n \"f1\": 0.4612814597315456,\n\ \ \"f1_stderr\": 0.004772539023607796,\n \"acc\": 0.3770824444865971,\n\ \ \"acc_stderr\": 0.007432066740076047\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.38873741610738255,\n \"em_stderr\": 0.004992082219869444,\n\ \ \"f1\": 0.4612814597315456,\n \"f1_stderr\": 0.004772539023607796\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \ \ \"acc_stderr\": 0.0026153265107756716\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n\ \ }\n}\n```" repo_url: https://huggingface.co/CalderaAI/13B-Thorns-l2 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_12T17_37_55.153820 path: - '**/details_harness|arc:challenge|25_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-12T17-37-55.153820.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_24T07_53_37.765793 path: - '**/details_harness|drop|3_2023-10-24T07-53-37.765793.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-24T07-53-37.765793.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_24T07_53_37.765793 path: - '**/details_harness|gsm8k|5_2023-10-24T07-53-37.765793.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-24T07-53-37.765793.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hellaswag|10_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T17-37-55.153820.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T17-37-55.153820.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T17_37_55.153820 path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T17-37-55.153820.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T17-37-55.153820.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_24T07_53_37.765793 path: - '**/details_harness|winogrande|5_2023-10-24T07-53-37.765793.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-24T07-53-37.765793.parquet' - config_name: results data_files: - split: 2023_09_12T17_37_55.153820 path: - results_2023-09-12T17-37-55.153820.parquet - split: 2023_10_24T07_53_37.765793 path: - results_2023-10-24T07-53-37.765793.parquet - split: latest path: - results_2023-10-24T07-53-37.765793.parquet --- # Dataset Card for Evaluation run of CalderaAI/13B-Thorns-l2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CalderaAI/13B-Thorns-l2 - **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 [CalderaAI/13B-Thorns-l2](https://huggingface.co/CalderaAI/13B-Thorns-l2) 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_CalderaAI__13B-Thorns-l2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T07:53:37.765793](https://huggingface.co/datasets/open-llm-leaderboard/details_CalderaAI__13B-Thorns-l2/blob/main/results_2023-10-24T07-53-37.765793.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": 0.38873741610738255, "em_stderr": 0.004992082219869444, "f1": 0.4612814597315456, "f1_stderr": 0.004772539023607796, "acc": 0.3770824444865971, "acc_stderr": 0.007432066740076047 }, "harness|drop|3": { "em": 0.38873741610738255, "em_stderr": 0.004992082219869444, "f1": 0.4612814597315456, "f1_stderr": 0.004772539023607796 }, "harness|gsm8k|5": { "acc": 0.009097801364670205, "acc_stderr": 0.0026153265107756716 }, "harness|winogrande|5": { "acc": 0.745067087608524, "acc_stderr": 0.012248806969376422 } } ``` ### 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]
mtek2000/hausa_topic_classification
--- license: mit ---
liuyanchen1015/MULTI_VALUE_mnli_null_relcl
--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 463647 num_examples: 1796 - name: dev_mismatched num_bytes: 549705 num_examples: 2124 - name: test_matched num_bytes: 497422 num_examples: 1941 - name: test_mismatched num_bytes: 532226 num_examples: 2111 - name: train num_bytes: 19712790 num_examples: 76988 download_size: 13857134 dataset_size: 21755790 --- # Dataset Card for "MULTI_VALUE_mnli_null_relcl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sam2ai/hindi_truthfulqa_gen_mini
--- configs: - config_name: default data_files: - split: validation path: data/validation-* dataset_info: features: - name: type dtype: string - name: category dtype: string - name: question dtype: string - name: best_answer dtype: string - name: correct_answers sequence: string - name: incorrect_answers sequence: string - name: source dtype: string splits: - name: validation num_bytes: 81430 num_examples: 50 download_size: 35995 dataset_size: 81430 --- # Dataset Card for "hindi_truthfulqa_gen_mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_SyedAbdul__test-7B-slerp
--- pretty_name: Evaluation run of SyedAbdul/test-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [SyedAbdul/test-7B-slerp](https://huggingface.co/SyedAbdul/test-7B-slerp) 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_SyedAbdul__test-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T13:37:15.686780](https://huggingface.co/datasets/open-llm-leaderboard/details_SyedAbdul__test-7B-slerp/blob/main/results_2024-01-04T13-37-15.686780.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\": 0.6489692801245827,\n\ \ \"acc_stderr\": 0.03208856045898211,\n \"acc_norm\": 0.649945556226307,\n\ \ \"acc_norm_stderr\": 0.03273989306811606,\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.01745464515097059,\n \"mc2\": 0.6259520494051883,\n\ \ \"mc2_stderr\": 0.014977076792645322\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6450511945392492,\n \"acc_stderr\": 0.01398303690409409,\n\ \ \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173311\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6744672376020713,\n\ \ \"acc_stderr\": 0.004676159299105416,\n \"acc_norm\": 0.8607847042421828,\n\ \ \"acc_norm_stderr\": 0.003454635760066236\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\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\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\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\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\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\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\ \ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.43386243386243384,\n \"acc_stderr\": 0.02552503438247489,\n \"\ acc_norm\": 0.43386243386243384,\n \"acc_norm_stderr\": 0.02552503438247489\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\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\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066496,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066496\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.029837962388291932,\n\ \ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.029837962388291932\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\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\": 0.027325470966716312,\n\ \ \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290913,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290913\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624734,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624734\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516303,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516303\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281376\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\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\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\": 0.3854748603351955,\n\ \ \"acc_stderr\": 0.016277927039638193,\n \"acc_norm\": 0.3854748603351955,\n\ \ \"acc_norm_stderr\": 0.016277927039638193\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7623456790123457,\n \"acc_stderr\": 0.02368359183700856,\n\ \ \"acc_norm\": 0.7623456790123457,\n \"acc_norm_stderr\": 0.02368359183700856\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4471968709256845,\n\ \ \"acc_stderr\": 0.012698825252435106,\n \"acc_norm\": 0.4471968709256845,\n\ \ \"acc_norm_stderr\": 0.012698825252435106\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6666666666666666,\n \"acc_stderr\": 0.019070985589687495,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.019070985589687495\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\": 0.7387755102040816,\n \"acc_stderr\": 0.02812342933514278,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.02812342933514278\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306032,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306032\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.01745464515097059,\n \"mc2\": 0.6259520494051883,\n\ \ \"mc2_stderr\": 0.014977076792645322\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6542835481425322,\n \ \ \"acc_stderr\": 0.01310042299044157\n }\n}\n```" repo_url: https://huggingface.co/SyedAbdul/test-7B-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|arc:challenge|25_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-04T13-37-15.686780.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|gsm8k|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hellaswag|10_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-37-15.686780.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-37-15.686780.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T13-37-15.686780.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_04T13_37_15.686780 path: - '**/details_harness|winogrande|5_2024-01-04T13-37-15.686780.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-04T13-37-15.686780.parquet' - config_name: results data_files: - split: 2024_01_04T13_37_15.686780 path: - results_2024-01-04T13-37-15.686780.parquet - split: latest path: - results_2024-01-04T13-37-15.686780.parquet --- # Dataset Card for Evaluation run of SyedAbdul/test-7B-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [SyedAbdul/test-7B-slerp](https://huggingface.co/SyedAbdul/test-7B-slerp) 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_SyedAbdul__test-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T13:37:15.686780](https://huggingface.co/datasets/open-llm-leaderboard/details_SyedAbdul__test-7B-slerp/blob/main/results_2024-01-04T13-37-15.686780.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": 0.6489692801245827, "acc_stderr": 0.03208856045898211, "acc_norm": 0.649945556226307, "acc_norm_stderr": 0.03273989306811606, "mc1": 0.46266829865361075, "mc1_stderr": 0.01745464515097059, "mc2": 0.6259520494051883, "mc2_stderr": 0.014977076792645322 }, "harness|arc:challenge|25": { "acc": 0.6450511945392492, "acc_stderr": 0.01398303690409409, "acc_norm": 0.6808873720136519, "acc_norm_stderr": 0.013621696119173311 }, "harness|hellaswag|10": { "acc": 0.6744672376020713, "acc_stderr": 0.004676159299105416, "acc_norm": 0.8607847042421828, "acc_norm_stderr": 0.003454635760066236 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "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": 0.7018867924528301, "acc_stderr": 0.028152837942493864, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493864 }, "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": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6473988439306358, "acc_stderr": 0.03643037168958548, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.03643037168958548 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108101, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108101 }, "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": 0.5724137931034483, "acc_stderr": 0.04122737111370332, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370332 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.43386243386243384, "acc_stderr": 0.02552503438247489, "acc_norm": 0.43386243386243384, "acc_norm_stderr": 0.02552503438247489 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7838709677419354, "acc_stderr": 0.023415293433568525, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.023415293433568525 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "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": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267045, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267045 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033456, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033456 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.028317533496066496, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.028317533496066496 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6974789915966386, "acc_stderr": 0.029837962388291932, "acc_norm": 0.6974789915966386, "acc_norm_stderr": 0.029837962388291932 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374303, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374303 }, "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": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290913, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290913 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624734, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624734 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7520661157024794, "acc_stderr": 0.03941897526516303, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.03941897526516303 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8333333333333334, "acc_stderr": 0.03602814176392645, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.03602814176392645 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281376, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281376 }, "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": 0.8339719029374202, "acc_stderr": 0.013306478243066302, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.013306478243066302 }, "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": 0.3854748603351955, "acc_stderr": 0.016277927039638193, "acc_norm": 0.3854748603351955, "acc_norm_stderr": 0.016277927039638193 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7623456790123457, "acc_stderr": 0.02368359183700856, "acc_norm": 0.7623456790123457, "acc_norm_stderr": 0.02368359183700856 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4471968709256845, "acc_stderr": 0.012698825252435106, "acc_norm": 0.4471968709256845, "acc_norm_stderr": 0.012698825252435106 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.019070985589687495, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.019070985589687495 }, "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": 0.7387755102040816, "acc_stderr": 0.02812342933514278, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.02812342933514278 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306032, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306032 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.03882310850890594, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.03882310850890594 }, "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": 0.46266829865361075, "mc1_stderr": 0.01745464515097059, "mc2": 0.6259520494051883, "mc2_stderr": 0.014977076792645322 }, "harness|winogrande|5": { "acc": 0.8082083662194159, "acc_stderr": 0.011065209664659527 }, "harness|gsm8k|5": { "acc": 0.6542835481425322, "acc_stderr": 0.01310042299044157 } } ``` ## 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]
ssbuild/alpaca_dolly
--- license: apache-2.0 ---
mlabonne/medical-mqca-fr
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* dataset_info: features: - name: Specialite dtype: string - name: Serie dtype: int64 - name: Question dtype: int64 - name: N_Question dtype: int64 - name: Answer dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4455800 num_examples: 3836 - name: eval num_bytes: 172116 num_examples: 150 download_size: 2123478 dataset_size: 4627916 --- # Dataset Card for "medical-mqca-fr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_automerger__Experiment28Yam-7B
--- pretty_name: Evaluation run of automerger/Experiment28Yam-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [automerger/Experiment28Yam-7B](https://huggingface.co/automerger/Experiment28Yam-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_automerger__Experiment28Yam-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-05T23:59:57.983132](https://huggingface.co/datasets/open-llm-leaderboard/details_automerger__Experiment28Yam-7B/blob/main/results_2024-04-05T23-59-57.983132.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\": 0.6501524355257138,\n\ \ \"acc_stderr\": 0.032105621193819094,\n \"acc_norm\": 0.6493107160751739,\n\ \ \"acc_norm_stderr\": 0.0327807693064778,\n \"mc1\": 0.6266829865361077,\n\ \ \"mc1_stderr\": 0.016932370557570638,\n \"mc2\": 0.782582180310156,\n\ \ \"mc2_stderr\": 0.013594678008386197\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.712457337883959,\n \"acc_stderr\": 0.013226719056266129,\n\ \ \"acc_norm\": 0.7261092150170648,\n \"acc_norm_stderr\": 0.013032004972989506\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7126070503883688,\n\ \ \"acc_stderr\": 0.004516215206715357,\n \"acc_norm\": 0.8911571400119498,\n\ \ \"acc_norm_stderr\": 0.003108054563352108\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7056603773584905,\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\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.05\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\": 0.6589595375722543,\n\ \ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\ \ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n\ \ \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7774193548387097,\n \"acc_stderr\": 0.023664216671642518,\n \"\ acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.023664216671642518\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7575757575757576,\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\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.0303883535518868,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.0303883535518868\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590167,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590167\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553346,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553346\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.02508596114457966,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.02508596114457966\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159464,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159464\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\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n\ \ \"acc_stderr\": 0.013586619219903347,\n \"acc_norm\": 0.8250319284802043,\n\ \ \"acc_norm_stderr\": 0.013586619219903347\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.02394851290546836,\n\ \ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.02394851290546836\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4324022346368715,\n\ \ \"acc_stderr\": 0.01656897123354861,\n \"acc_norm\": 0.4324022346368715,\n\ \ \"acc_norm_stderr\": 0.01656897123354861\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.025917806117147158,\n\ \ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.025917806117147158\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035454,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035454\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47196870925684486,\n\ \ \"acc_stderr\": 0.012750151802922438,\n \"acc_norm\": 0.47196870925684486,\n\ \ \"acc_norm_stderr\": 0.012750151802922438\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\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\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\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\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\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\": 0.6266829865361077,\n\ \ \"mc1_stderr\": 0.016932370557570638,\n \"mc2\": 0.782582180310156,\n\ \ \"mc2_stderr\": 0.013594678008386197\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8484609313338595,\n \"acc_stderr\": 0.010077698907571764\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6937073540561031,\n \ \ \"acc_stderr\": 0.012696930106562912\n }\n}\n```" repo_url: https://huggingface.co/automerger/Experiment28Yam-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|arc:challenge|25_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-05T23-59-57.983132.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|gsm8k|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hellaswag|10_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-05T23-59-57.983132.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-management|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T23-59-57.983132.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|truthfulqa:mc|0_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-05T23-59-57.983132.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_05T23_59_57.983132 path: - '**/details_harness|winogrande|5_2024-04-05T23-59-57.983132.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-05T23-59-57.983132.parquet' - config_name: results data_files: - split: 2024_04_05T23_59_57.983132 path: - results_2024-04-05T23-59-57.983132.parquet - split: latest path: - results_2024-04-05T23-59-57.983132.parquet --- # Dataset Card for Evaluation run of automerger/Experiment28Yam-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [automerger/Experiment28Yam-7B](https://huggingface.co/automerger/Experiment28Yam-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_automerger__Experiment28Yam-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-05T23:59:57.983132](https://huggingface.co/datasets/open-llm-leaderboard/details_automerger__Experiment28Yam-7B/blob/main/results_2024-04-05T23-59-57.983132.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": 0.6501524355257138, "acc_stderr": 0.032105621193819094, "acc_norm": 0.6493107160751739, "acc_norm_stderr": 0.0327807693064778, "mc1": 0.6266829865361077, "mc1_stderr": 0.016932370557570638, "mc2": 0.782582180310156, "mc2_stderr": 0.013594678008386197 }, "harness|arc:challenge|25": { "acc": 0.712457337883959, "acc_stderr": 0.013226719056266129, "acc_norm": 0.7261092150170648, "acc_norm_stderr": 0.013032004972989506 }, "harness|hellaswag|10": { "acc": 0.7126070503883688, "acc_stderr": 0.004516215206715357, "acc_norm": 0.8911571400119498, "acc_norm_stderr": 0.003108054563352108 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "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": 0.6589595375722543, "acc_stderr": 0.03614665424180826, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.03614665424180826 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082636, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082636 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099835, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099835 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923996, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923996 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "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": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.8080808080808081, "acc_stderr": 0.028057791672989017, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.028057791672989017 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.021995311963644237, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.021995311963644237 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028593, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028593 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.0303883535518868, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.0303883535518868 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.37748344370860926, "acc_stderr": 0.03958027231121569, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.03958027231121569 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8440366972477065, "acc_stderr": 0.015555802713590167, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590167 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.025524722324553346, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.025524722324553346 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8185654008438819, "acc_stderr": 0.02508596114457966, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.02508596114457966 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159464, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159464 }, "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": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8250319284802043, "acc_stderr": 0.013586619219903347, "acc_norm": 0.8250319284802043, "acc_norm_stderr": 0.013586619219903347 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7283236994219653, "acc_stderr": 0.02394851290546836, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.02394851290546836 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4324022346368715, "acc_stderr": 0.01656897123354861, "acc_norm": 0.4324022346368715, "acc_norm_stderr": 0.01656897123354861 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7124183006535948, "acc_stderr": 0.025917806117147158, "acc_norm": 0.7124183006535948, "acc_norm_stderr": 0.025917806117147158 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7407407407407407, "acc_stderr": 0.024383665531035454, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.024383665531035454 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47196870925684486, "acc_stderr": 0.012750151802922438, "acc_norm": 0.47196870925684486, "acc_norm_stderr": 0.012750151802922438 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462927, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462927 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "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": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "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": 0.6266829865361077, "mc1_stderr": 0.016932370557570638, "mc2": 0.782582180310156, "mc2_stderr": 0.013594678008386197 }, "harness|winogrande|5": { "acc": 0.8484609313338595, "acc_stderr": 0.010077698907571764 }, "harness|gsm8k|5": { "acc": 0.6937073540561031, "acc_stderr": 0.012696930106562912 } } ``` ## 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]
anechaev/med_history
--- license: mit ---
ucla-contextual/contextual_test
--- configs: - config_name: default data_files: - split: test path: "contextual_test.csv" --- --- license: mit --- Check out the [paper](https://arxiv.org/abs/2401.13311).
GARDA/customsmkcode
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5826 num_examples: 39 download_size: 2572 dataset_size: 5826 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "customsmkcode" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/c6b5396f
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1338 dataset_size: 184 --- # Dataset Card for "c6b5396f" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DylanJHJ/pds2023
--- license: apache-2.0 ---
autoevaluate/autoeval-eval-futin__random-en-30c46b-2023566786
--- type: predictions tags: - autotrain - evaluation datasets: - futin/random eval_info: task: text_zero_shot_classification model: facebook/opt-13b metrics: [] dataset_name: futin/random dataset_config: 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-13b * Dataset: futin/random * Config: 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/kitazawa_shiho_theidolmstermillionlive
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kitazawa_shiho/北沢志保/키타자와시호 (THE iDOLM@STER: Million Live!) This is the dataset of kitazawa_shiho/北沢志保/키타자와시호 (THE iDOLM@STER: Million Live!), containing 500 images and their tags. The core tags of this character are `long_hair, brown_hair, brown_eyes, breasts, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:------------|:----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 579.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 348.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1170 | 727.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 520.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1170 | 1014.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/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/kitazawa_shiho_theidolmstermillionlive', 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 | 10 | ![](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, cat_ears, cat_tail, jingle_bell, looking_at_viewer, solo, long_sleeves, paw_gloves, black_dress, blush, cleavage, fur_trim, neck_bell, purple_bow, cat_paws, simple_background, white_background, open_mouth, shiny_hair, blue_bowtie, closed_mouth, frilled_dress, paw_shoes, ribbon, yellow_eyes | | 1 | 9 | ![](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, looking_at_viewer, closed_mouth, collarbone, short_sleeves, simple_background, solo, striped_shirt, upper_body, blush, white_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) | 1girl, smile, solo, blush, looking_at_viewer | | 3 | 5 | ![](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, cleavage, collarbone, looking_at_viewer, medium_breasts, solo, blush, floral_print, lingerie, closed_mouth, shiny_hair, smile, yellow_eyes, babydoll, bare_shoulders, detached_sleeves, on_side, panties, underwear_only | | 4 | 20 | ![](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, blush, solo, navel, looking_at_viewer, nipples, medium_breasts, female_pubic_hair, collarbone, closed_mouth, completely_nude, simple_background, stomach, upper_body | | 5 | 8 | ![](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) | blush, day, looking_at_viewer, outdoors, 1girl, medium_breasts, ocean, solo, cleavage, collarbone, closed_mouth, cloud, navel, side-tie_bikini_bottom, blue_sky, cowboy_shot, lens_flare, standing, water, wet, yellow_eyes, bare_shoulders, beach, black_bikini, frills, front-tie_bikini_top, halterneck, hand_up, smile, wading, white_bikini | | 6 | 6 | ![](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, black_serafuku, looking_at_viewer, solo, black_shirt, black_skirt, red_neckerchief, black_gloves, black_sailor_collar, pleated_skirt, short_sleeves, closed_mouth, fingerless_gloves, standing | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, blush, looking_at_viewer, smile, solo, dress, holding_microphone, blurry, bow, open_mouth, frilled_sleeves, hair_ribbon, juliet_sleeves, upper_body, wrist_cuffs, yellow_eyes | | 8 | 30 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1boy, 1girl, hetero, solo_focus, blush, nipples, penis, sex, sweat, vaginal, medium_breasts, open_mouth, navel, looking_at_viewer, pussy, completely_nude, female_pubic_hair, mosaic_censoring, straddling | | 9 | 9 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, solo, closed_mouth, sleeveless, black_shorts, hat, looking_at_viewer, shiny_hair, short_shorts, blue_headwear, detached_sleeves, floating_hair, standing, dress, frills, striped, very_long_hair, black_sleeves, bow, cowboy_shot, long_sleeves, smile, thighhighs, yellow_eyes | | 10 | 8 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, looking_at_viewer, maid_apron, maid_headdress, solo, frills, wa_maid, long_sleeves, wide_sleeves, medical_eyepatch, outdoors, black_kimono, holding_weapon, knife, night, parted_lips, sky, upper_body, white_apron | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cat_ears | cat_tail | jingle_bell | looking_at_viewer | solo | long_sleeves | paw_gloves | black_dress | blush | cleavage | fur_trim | neck_bell | purple_bow | cat_paws | simple_background | white_background | open_mouth | shiny_hair | blue_bowtie | closed_mouth | frilled_dress | paw_shoes | ribbon | yellow_eyes | collarbone | short_sleeves | striped_shirt | upper_body | smile | medium_breasts | floral_print | lingerie | babydoll | bare_shoulders | detached_sleeves | on_side | panties | underwear_only | navel | nipples | female_pubic_hair | completely_nude | stomach | day | outdoors | ocean | cloud | side-tie_bikini_bottom | blue_sky | cowboy_shot | lens_flare | standing | water | wet | beach | black_bikini | frills | front-tie_bikini_top | halterneck | hand_up | wading | white_bikini | black_serafuku | black_shirt | black_skirt | red_neckerchief | black_gloves | black_sailor_collar | pleated_skirt | fingerless_gloves | dress | holding_microphone | blurry | bow | frilled_sleeves | hair_ribbon | juliet_sleeves | wrist_cuffs | 1boy | hetero | solo_focus | penis | sex | sweat | vaginal | pussy | mosaic_censoring | straddling | sleeveless | black_shorts | hat | short_shorts | blue_headwear | floating_hair | striped | very_long_hair | black_sleeves | thighhighs | maid_apron | maid_headdress | wa_maid | wide_sleeves | medical_eyepatch | black_kimono | holding_weapon | knife | night | parted_lips | sky | white_apron | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------|:-----------|:--------------|:--------------------|:-------|:---------------|:-------------|:--------------|:--------|:-----------|:-----------|:------------|:-------------|:-----------|:--------------------|:-------------------|:-------------|:-------------|:--------------|:---------------|:----------------|:------------|:---------|:--------------|:-------------|:----------------|:----------------|:-------------|:--------|:-----------------|:---------------|:-----------|:-----------|:-----------------|:-------------------|:----------|:----------|:-----------------|:--------|:----------|:--------------------|:------------------|:----------|:------|:-----------|:--------|:--------|:-------------------------|:-----------|:--------------|:-------------|:-----------|:--------|:------|:--------|:---------------|:---------|:-----------------------|:-------------|:----------|:---------|:---------------|:-----------------|:--------------|:--------------|:------------------|:---------------|:----------------------|:----------------|:--------------------|:--------|:---------------------|:---------|:------|:------------------|:--------------|:-----------------|:--------------|:-------|:---------|:-------------|:--------|:------|:--------|:----------|:--------|:-------------------|:-------------|:-------------|:---------------|:------|:---------------|:----------------|:----------------|:----------|:-----------------|:----------------|:-------------|:-------------|:-----------------|:----------|:---------------|:-------------------|:---------------|:-----------------|:--------|:--------|:--------------|:------|:--------------| | 0 | 10 | ![](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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 9 | ![](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 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 20 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 8 | ![](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 | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 6 | ![](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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | | X | X | | | | X | | | | | | | | X | | | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 30 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | | X | | | | | X | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 9 | 9 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-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 | | | | | | | | | | | | | | 10 | 8 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
czczycz/QABot
--- license: openrail ---
RissoleDekejo/Bubsy
--- license: openrail ---
liuyanchen1015/MULTI_VALUE_qqp_analytic_superlative
--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 866229 num_examples: 5699 - name: test num_bytes: 8686312 num_examples: 55717 - name: train num_bytes: 7958668 num_examples: 52303 download_size: 9064404 dataset_size: 17511209 --- # Dataset Card for "MULTI_VALUE_qqp_analytic_superlative" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Expert68__llama2_13b_instructed_version2
--- pretty_name: Evaluation run of Expert68/llama2_13b_instructed_version2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Expert68/llama2_13b_instructed_version2](https://huggingface.co/Expert68/llama2_13b_instructed_version2)\ \ 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 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_Expert68__llama2_13b_instructed_version2_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-12T19:44:39.658427](https://huggingface.co/datasets/open-llm-leaderboard/details_Expert68__llama2_13b_instructed_version2_public/blob/main/results_2023-11-12T19-44-39.658427.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\": 0.5535385938067054,\n\ \ \"acc_stderr\": 0.03382379046360409,\n \"acc_norm\": 0.5616374813808622,\n\ \ \"acc_norm_stderr\": 0.034597480068222046,\n \"mc1\": 0.31456548347613217,\n\ \ \"mc1_stderr\": 0.01625524199317918,\n \"mc2\": 0.46118545589659976,\n\ \ \"mc2_stderr\": 0.015483508114692393,\n \"em\": 0.007340604026845637,\n\ \ \"em_stderr\": 0.0008741896875345934,\n \"f1\": 0.07567323825503336,\n\ \ \"f1_stderr\": 0.0016747744191590948\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5631399317406144,\n \"acc_stderr\": 0.014494421584256519,\n\ \ \"acc_norm\": 0.6006825938566553,\n \"acc_norm_stderr\": 0.014312094557946705\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6412069308902609,\n\ \ \"acc_stderr\": 0.004786660691181909,\n \"acc_norm\": 0.8404700258912567,\n\ \ \"acc_norm_stderr\": 0.003654212329516619\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n\ \ \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n\ \ \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5460526315789473,\n \"acc_stderr\": 0.04051646342874142,\n\ \ \"acc_norm\": 0.5460526315789473,\n \"acc_norm_stderr\": 0.04051646342874142\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\": 0.5924528301886792,\n \"acc_stderr\": 0.030242233800854494,\n\ \ \"acc_norm\": 0.5924528301886792,\n \"acc_norm_stderr\": 0.030242233800854494\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.625,\n\ \ \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.625,\n \ \ \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5144508670520231,\n\ \ \"acc_stderr\": 0.03810871630454764,\n \"acc_norm\": 0.5144508670520231,\n\ \ \"acc_norm_stderr\": 0.03810871630454764\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364397,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364397\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.451063829787234,\n \"acc_stderr\": 0.032529096196131965,\n\ \ \"acc_norm\": 0.451063829787234,\n \"acc_norm_stderr\": 0.032529096196131965\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.32275132275132273,\n \"acc_stderr\": 0.024078943243597016,\n \"\ acc_norm\": 0.32275132275132273,\n \"acc_norm_stderr\": 0.024078943243597016\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\ \ \"acc_stderr\": 0.042857142857142816,\n \"acc_norm\": 0.35714285714285715,\n\ \ \"acc_norm_stderr\": 0.042857142857142816\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6483870967741936,\n\ \ \"acc_stderr\": 0.02716253782694846,\n \"acc_norm\": 0.6483870967741936,\n\ \ \"acc_norm_stderr\": 0.02716253782694846\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.43842364532019706,\n \"acc_stderr\": 0.03491207857486518,\n\ \ \"acc_norm\": 0.43842364532019706,\n \"acc_norm_stderr\": 0.03491207857486518\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091707,\n\ \ \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091707\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6717171717171717,\n \"acc_stderr\": 0.03345678422756776,\n \"\ acc_norm\": 0.6717171717171717,\n \"acc_norm_stderr\": 0.03345678422756776\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n\ \ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5153846153846153,\n \"acc_stderr\": 0.025339003010106515,\n\ \ \"acc_norm\": 0.5153846153846153,\n \"acc_norm_stderr\": 0.025339003010106515\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073835,\n \ \ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073835\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.592436974789916,\n \"acc_stderr\": 0.031918633744784645,\n \ \ \"acc_norm\": 0.592436974789916,\n \"acc_norm_stderr\": 0.031918633744784645\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.728440366972477,\n \"acc_stderr\": 0.019069098363191428,\n \"\ acc_norm\": 0.728440366972477,\n \"acc_norm_stderr\": 0.019069098363191428\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.42592592592592593,\n \"acc_stderr\": 0.03372343271653064,\n \"\ acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.03372343271653064\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7745098039215687,\n \"acc_stderr\": 0.02933116229425174,\n \"\ acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.02933116229425174\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7383966244725738,\n \"acc_stderr\": 0.028609516716994934,\n \ \ \"acc_norm\": 0.7383966244725738,\n \"acc_norm_stderr\": 0.028609516716994934\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\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\": 0.5801526717557252,\n \"acc_stderr\": 0.04328577215262971,\n\ \ \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.04328577215262971\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.041391127276354626,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.041391127276354626\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.044531975073749834,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.044531975073749834\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\ \ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\ \ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\ \ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.04453254836326467,\n\ \ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.04453254836326467\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n\ \ \"acc_stderr\": 0.025140935950335442,\n \"acc_norm\": 0.8205128205128205,\n\ \ \"acc_norm_stderr\": 0.025140935950335442\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7637292464878672,\n\ \ \"acc_stderr\": 0.01519047371703751,\n \"acc_norm\": 0.7637292464878672,\n\ \ \"acc_norm_stderr\": 0.01519047371703751\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6242774566473989,\n \"acc_stderr\": 0.02607431485165708,\n\ \ \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.02607431485165708\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4011173184357542,\n\ \ \"acc_stderr\": 0.01639222189940707,\n \"acc_norm\": 0.4011173184357542,\n\ \ \"acc_norm_stderr\": 0.01639222189940707\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5915032679738562,\n \"acc_stderr\": 0.028146405993096358,\n\ \ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.028146405993096358\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6527331189710611,\n\ \ \"acc_stderr\": 0.027040745502307336,\n \"acc_norm\": 0.6527331189710611,\n\ \ \"acc_norm_stderr\": 0.027040745502307336\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6604938271604939,\n \"acc_stderr\": 0.026348564412011624,\n\ \ \"acc_norm\": 0.6604938271604939,\n \"acc_norm_stderr\": 0.026348564412011624\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.43617021276595747,\n \"acc_stderr\": 0.02958345203628407,\n \ \ \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.02958345203628407\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4380704041720991,\n\ \ \"acc_stderr\": 0.01267190278256765,\n \"acc_norm\": 0.4380704041720991,\n\ \ \"acc_norm_stderr\": 0.01267190278256765\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5330882352941176,\n \"acc_stderr\": 0.03030625772246831,\n\ \ \"acc_norm\": 0.5330882352941176,\n \"acc_norm_stderr\": 0.03030625772246831\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5751633986928104,\n \"acc_stderr\": 0.01999797303545833,\n \ \ \"acc_norm\": 0.5751633986928104,\n \"acc_norm_stderr\": 0.01999797303545833\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\ \ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\ \ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03136250240935893,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03136250240935893\n },\n\ \ \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7164179104477612,\n\ \ \"acc_stderr\": 0.031871875379197966,\n \"acc_norm\": 0.7164179104477612,\n\ \ \"acc_norm_stderr\": 0.031871875379197966\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.03246721765117826,\n\ \ \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.03246721765117826\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31456548347613217,\n\ \ \"mc1_stderr\": 0.01625524199317918,\n \"mc2\": 0.46118545589659976,\n\ \ \"mc2_stderr\": 0.015483508114692393\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7561168113654302,\n \"acc_stderr\": 0.012068923278908194\n\ \ },\n \"harness|drop|3\": {\n \"em\": 0.007340604026845637,\n \ \ \"em_stderr\": 0.0008741896875345934,\n \"f1\": 0.07567323825503336,\n\ \ \"f1_stderr\": 0.0016747744191590948\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.10993176648976498,\n \"acc_stderr\": 0.008616195587865397\n\ \ }\n}\n```" repo_url: https://huggingface.co/Expert68/llama2_13b_instructed_version2 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_11_12T19_44_39.658427 path: - '**/details_harness|arc:challenge|25_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-11-12T19-44-39.658427.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|drop|3_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-12T19-44-39.658427.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|gsm8k|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hellaswag|10_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-12T19-44-39.658427.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-management|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-virology|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-12T19-44-39.658427.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|truthfulqa:mc|0_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-11-12T19-44-39.658427.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_12T19_44_39.658427 path: - '**/details_harness|winogrande|5_2023-11-12T19-44-39.658427.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-12T19-44-39.658427.parquet' - config_name: results data_files: - split: 2023_11_12T19_44_39.658427 path: - results_2023-11-12T19-44-39.658427.parquet - split: latest path: - results_2023-11-12T19-44-39.658427.parquet --- # Dataset Card for Evaluation run of Expert68/llama2_13b_instructed_version2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Expert68/llama2_13b_instructed_version2 - **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 [Expert68/llama2_13b_instructed_version2](https://huggingface.co/Expert68/llama2_13b_instructed_version2) 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 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_Expert68__llama2_13b_instructed_version2_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-12T19:44:39.658427](https://huggingface.co/datasets/open-llm-leaderboard/details_Expert68__llama2_13b_instructed_version2_public/blob/main/results_2023-11-12T19-44-39.658427.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": 0.5535385938067054, "acc_stderr": 0.03382379046360409, "acc_norm": 0.5616374813808622, "acc_norm_stderr": 0.034597480068222046, "mc1": 0.31456548347613217, "mc1_stderr": 0.01625524199317918, "mc2": 0.46118545589659976, "mc2_stderr": 0.015483508114692393, "em": 0.007340604026845637, "em_stderr": 0.0008741896875345934, "f1": 0.07567323825503336, "f1_stderr": 0.0016747744191590948 }, "harness|arc:challenge|25": { "acc": 0.5631399317406144, "acc_stderr": 0.014494421584256519, "acc_norm": 0.6006825938566553, "acc_norm_stderr": 0.014312094557946705 }, "harness|hellaswag|10": { "acc": 0.6412069308902609, "acc_stderr": 0.004786660691181909, "acc_norm": 0.8404700258912567, "acc_norm_stderr": 0.003654212329516619 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4740740740740741, "acc_stderr": 0.04313531696750574, "acc_norm": 0.4740740740740741, "acc_norm_stderr": 0.04313531696750574 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5460526315789473, "acc_stderr": 0.04051646342874142, "acc_norm": 0.5460526315789473, "acc_norm_stderr": 0.04051646342874142 }, "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": 0.5924528301886792, "acc_stderr": 0.030242233800854494, "acc_norm": 0.5924528301886792, "acc_norm_stderr": 0.030242233800854494 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.625, "acc_stderr": 0.04048439222695598, "acc_norm": 0.625, "acc_norm_stderr": 0.04048439222695598 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5144508670520231, "acc_stderr": 0.03810871630454764, "acc_norm": 0.5144508670520231, "acc_norm_stderr": 0.03810871630454764 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364397, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364397 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.451063829787234, "acc_stderr": 0.032529096196131965, "acc_norm": 0.451063829787234, "acc_norm_stderr": 0.032529096196131965 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.044346007015849245, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.044346007015849245 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.32275132275132273, "acc_stderr": 0.024078943243597016, "acc_norm": 0.32275132275132273, "acc_norm_stderr": 0.024078943243597016 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.35714285714285715, "acc_stderr": 0.042857142857142816, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.042857142857142816 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6483870967741936, "acc_stderr": 0.02716253782694846, "acc_norm": 0.6483870967741936, "acc_norm_stderr": 0.02716253782694846 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.43842364532019706, "acc_stderr": 0.03491207857486518, "acc_norm": 0.43842364532019706, "acc_norm_stderr": 0.03491207857486518 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.696969696969697, "acc_stderr": 0.03588624800091707, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.03588624800091707 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6717171717171717, "acc_stderr": 0.03345678422756776, "acc_norm": 0.6717171717171717, "acc_norm_stderr": 0.03345678422756776 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548057, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548057 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5153846153846153, "acc_stderr": 0.025339003010106515, "acc_norm": 0.5153846153846153, "acc_norm_stderr": 0.025339003010106515 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26666666666666666, "acc_stderr": 0.026962424325073835, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.026962424325073835 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.592436974789916, "acc_stderr": 0.031918633744784645, "acc_norm": 0.592436974789916, "acc_norm_stderr": 0.031918633744784645 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.728440366972477, "acc_stderr": 0.019069098363191428, "acc_norm": 0.728440366972477, "acc_norm_stderr": 0.019069098363191428 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.42592592592592593, "acc_stderr": 0.03372343271653064, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.03372343271653064 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7745098039215687, "acc_stderr": 0.02933116229425174, "acc_norm": 0.7745098039215687, "acc_norm_stderr": 0.02933116229425174 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7383966244725738, "acc_stderr": 0.028609516716994934, "acc_norm": 0.7383966244725738, "acc_norm_stderr": 0.028609516716994934 }, "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": 0.5801526717557252, "acc_stderr": 0.04328577215262971, "acc_norm": 0.5801526717557252, "acc_norm_stderr": 0.04328577215262971 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7107438016528925, "acc_stderr": 0.041391127276354626, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.041391127276354626 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6944444444444444, "acc_stderr": 0.044531975073749834, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.044531975073749834 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6687116564417178, "acc_stderr": 0.03697983910025588, "acc_norm": 0.6687116564417178, "acc_norm_stderr": 0.03697983910025588 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.39285714285714285, "acc_stderr": 0.04635550135609976, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.04635550135609976 }, "harness|hendrycksTest-management|5": { "acc": 0.7184466019417476, "acc_stderr": 0.04453254836326467, "acc_norm": 0.7184466019417476, "acc_norm_stderr": 0.04453254836326467 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8205128205128205, "acc_stderr": 0.025140935950335442, "acc_norm": 0.8205128205128205, "acc_norm_stderr": 0.025140935950335442 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7637292464878672, "acc_stderr": 0.01519047371703751, "acc_norm": 0.7637292464878672, "acc_norm_stderr": 0.01519047371703751 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6242774566473989, "acc_stderr": 0.02607431485165708, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.02607431485165708 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4011173184357542, "acc_stderr": 0.01639222189940707, "acc_norm": 0.4011173184357542, "acc_norm_stderr": 0.01639222189940707 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5915032679738562, "acc_stderr": 0.028146405993096358, "acc_norm": 0.5915032679738562, "acc_norm_stderr": 0.028146405993096358 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6527331189710611, "acc_stderr": 0.027040745502307336, "acc_norm": 0.6527331189710611, "acc_norm_stderr": 0.027040745502307336 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6604938271604939, "acc_stderr": 0.026348564412011624, "acc_norm": 0.6604938271604939, "acc_norm_stderr": 0.026348564412011624 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.43617021276595747, "acc_stderr": 0.02958345203628407, "acc_norm": 0.43617021276595747, "acc_norm_stderr": 0.02958345203628407 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4380704041720991, "acc_stderr": 0.01267190278256765, "acc_norm": 0.4380704041720991, "acc_norm_stderr": 0.01267190278256765 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5330882352941176, "acc_stderr": 0.03030625772246831, "acc_norm": 0.5330882352941176, "acc_norm_stderr": 0.03030625772246831 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5751633986928104, "acc_stderr": 0.01999797303545833, "acc_norm": 0.5751633986928104, "acc_norm_stderr": 0.01999797303545833 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6363636363636364, "acc_stderr": 0.04607582090719976, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.04607582090719976 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6, "acc_stderr": 0.03136250240935893, "acc_norm": 0.6, "acc_norm_stderr": 0.03136250240935893 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7164179104477612, "acc_stderr": 0.031871875379197966, "acc_norm": 0.7164179104477612, "acc_norm_stderr": 0.031871875379197966 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.45180722891566266, "acc_stderr": 0.03874371556587953, "acc_norm": 0.45180722891566266, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7660818713450293, "acc_stderr": 0.03246721765117826, "acc_norm": 0.7660818713450293, "acc_norm_stderr": 0.03246721765117826 }, "harness|truthfulqa:mc|0": { "mc1": 0.31456548347613217, "mc1_stderr": 0.01625524199317918, "mc2": 0.46118545589659976, "mc2_stderr": 0.015483508114692393 }, "harness|winogrande|5": { "acc": 0.7561168113654302, "acc_stderr": 0.012068923278908194 }, "harness|drop|3": { "em": 0.007340604026845637, "em_stderr": 0.0008741896875345934, "f1": 0.07567323825503336, "f1_stderr": 0.0016747744191590948 }, "harness|gsm8k|5": { "acc": 0.10993176648976498, "acc_stderr": 0.008616195587865397 } } ``` ### 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]
OdiaGenAI/roleplay_english
--- task_categories: - question-answering - conversational language: - en size_categories: - 1K<n<10K ---
nicholasbien/lakh-txt-full-v2-gpt2-tokenized
--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 1554119120 num_examples: 13560 - name: test num_bytes: 385501195 num_examples: 3390 download_size: 662698287 dataset_size: 1939620315 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
sugeun/summaryTest
--- license: apache-2.0 ---
cvzion/dqg-dataset-v4-final
--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 71406 num_examples: 131 download_size: 28648 dataset_size: 71406 configs: - config_name: default data_files: - split: train path: data/train-* ---
BubbleJoe/mscoco_simplified
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: restval path: data/restval-* - split: validation path: data/validation-* dataset_info: features: - name: sentids dtype: int64 - name: sentences dtype: string - name: simplified dtype: string splits: - name: train num_bytes: 37370437 num_examples: 414113 - name: test num_bytes: 2252431 num_examples: 25010 - name: restval num_bytes: 13747474 num_examples: 152634 - name: validation num_bytes: 2254719 num_examples: 25010 download_size: 29875182 dataset_size: 55625061 --- # Dataset Card for "mscoco_simplified" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akorson/allenai
--- license: openrail ---
CyberHarem/nyotengu_azurlane
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nyotengu/女天狗/女天狗 (Azur Lane) This is the dataset of nyotengu/女天狗/女天狗 (Azur Lane), containing 191 images and their tags. The core tags of this character are `black_hair, breasts, large_breasts, long_hair, mole, purple_eyes, mole_under_mouth, bangs, blunt_bangs, hime_cut, hair_ornament`, 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 | 191 | 231.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyotengu_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 191 | 143.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyotengu_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 434 | 281.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyotengu_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 191 | 211.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyotengu_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 434 | 380.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nyotengu_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/nyotengu_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 | 28 | ![](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, detached_sleeves, looking_at_viewer, cleavage, bare_shoulders, black_wings, tokin_hat, hand_fan, feathered_wings, makeup, smile, kimono, tengu, sash, hauchiwa, tongue_out, lips | | 1 | 22 | ![](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, looking_at_viewer, solo, cleavage, bare_shoulders, navel, parted_lips, smile, o-ring_bikini, water, fingerless_gloves, black_bikini, collarbone, wet | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | detached_sleeves | looking_at_viewer | cleavage | bare_shoulders | black_wings | tokin_hat | hand_fan | feathered_wings | makeup | smile | kimono | tengu | sash | hauchiwa | tongue_out | lips | navel | parted_lips | o-ring_bikini | water | fingerless_gloves | black_bikini | collarbone | wet | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------------|:--------------------|:-----------|:-----------------|:--------------|:------------|:-----------|:------------------|:---------|:--------|:---------|:--------|:-------|:-----------|:-------------|:-------|:--------|:--------------|:----------------|:--------|:--------------------|:---------------|:-------------|:------| | 0 | 28 | ![](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 | | | | | | | | | | 1 | 22 | ![](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 |
FarAwayFer/gua-llama-ofan
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1656684 num_examples: 1008 download_size: 970097 dataset_size: 1656684 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gua-llama-ofan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
YuehHanChen/sst2_finetuning_dataset
--- dataset_info: features: - name: sentence dtype: string - name: answer dtype: int64 splits: - name: train num_bytes: 11574670 num_examples: 68221 download_size: 0 dataset_size: 11574670 --- # Dataset Card for "sst2_finetuning_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
slayone/uva_spoj_raw
--- license: mit task_categories: - translation - text-generation tags: - code size_categories: - 1K<n<10K ---
udayl/rocks
--- license: mit --- Rocks dataset with 7 classes: [Coal, Limestone, Marble, Sandstone, Quartzite, Basalt, Granite]
DONG19/instruct_code_search_net
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 1974467839.5874639 num_examples: 1629456 - name: validation num_bytes: 93239061.8591426 num_examples: 76505 - name: test num_bytes: 104426710.35366909 num_examples: 87036 download_size: 652473629 dataset_size: 2172133611.8002753 --- # Dataset Card for "instruct_code_search_net" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BangumiBase/akatsukinoyona
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Akatsuki No Yona This is the image base of bangumi Akatsuki no Yona, we detected 41 characters, 3412 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 532 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 33 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 76 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 69 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 39 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 34 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 18 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 213 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 46 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 207 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 29 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 58 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 50 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 60 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 35 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 58 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 28 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 15 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 15 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 230 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 57 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 22 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 85 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 31 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 21 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 25 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 9 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 21 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 797 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 77 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 11 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 7 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | N/A | | 32 | 14 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 26 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 41 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 14 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | ![preview 8](35/preview_8.png) | | 36 | 6 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | N/A | N/A | | 37 | 14 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | ![preview 8](37/preview_8.png) | | 38 | 9 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 46 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | noise | 234 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
Brizape/tmvar_split_0404_dev
--- dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: texts dtype: string splits: - name: train num_bytes: 1614127.7155688624 num_examples: 801 - name: validation num_bytes: 405043.2844311377 num_examples: 201 - name: test num_bytes: 977708 num_examples: 498 download_size: 883485 dataset_size: 2996879.0 --- # Dataset Card for "tmvar_split_0404_dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_cola_dey_it
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1545 num_examples: 22 - name: test num_bytes: 1597 num_examples: 21 - name: train num_bytes: 10190 num_examples: 146 download_size: 12452 dataset_size: 13332 --- # Dataset Card for "VALUE_cola_dey_it" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jonathanasdf/MathGLM-dataset-500k
--- license: afl-3.0 --- Every 100th row from https://github.com/THUDM/MathGLM (original dataset has 50M entries)
zolak/twitter_dataset_81_1713184381
--- 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: 228697 num_examples: 552 download_size: 122639 dataset_size: 228697 configs: - config_name: default data_files: - split: train path: data/train-* ---
maximedb/natural_questions
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 10087609 num_examples: 130233 - name: validation num_bytes: 714323 num_examples: 8643 download_size: 6827128 dataset_size: 10801932 --- # Dataset Card for "natural_questions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
madha98/Shakespeare
--- license: mit ---
sheik21/voz-ronaldo
--- license: openrail ---
RamonPereira/minhavoz98
--- license: openrail ---
Ramitha/open-australian-legal-qa-test-analysis
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: original_texts sequence: int64 - name: is_knowledge_available dtype: string - name: llm_knowledge_document dtype: string splits: - name: test num_bytes: 55142 num_examples: 35 download_size: 41463 dataset_size: 55142 configs: - config_name: default data_files: - split: test path: data/test-* ---
liuyanchen1015/MULTI_VALUE_mrpc_your_yalls
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 895 num_examples: 3 - name: train num_bytes: 1605 num_examples: 6 - name: validation num_bytes: 225 num_examples: 1 download_size: 12395 dataset_size: 2725 --- # Dataset Card for "MULTI_VALUE_mrpc_your_yalls" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CamiloVega/Llama2-jobsedcription-requirement
--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 3964110 num_examples: 500 download_size: 1691632 dataset_size: 3964110 configs: - config_name: default data_files: - split: train path: data/train-* ---
m720/SHADR
--- license: cc-by-4.0 task_categories: - text-classification language: - en tags: - medical pretty_name: SHADR size_categories: - 1K<n<10K --- # SDoH Human Annotated Demographic Robustness (SHADR) Dataset ## Overview The Social determinants of health (SDoH) play a pivotal role in determining patient outcomes. However, their documentation in electronic health records (EHR) remains incomplete. This dataset was created from a study examining the capability of large language models in extracting SDoH from the free text sections of EHRs. Furthermore, the study delved into the potential of synthetic clinical text to bolster the extraction process of these scarcely documented, yet crucial, clinical data. ## Dataset Structure & Modification To understand potential biases in high-performing models and in those pre-trained on general text, GPT-4 was utilized to infuse demographic descriptors into our synthetic data. For instance: - **Original Sentence**: "Widower admits fears surrounding potential judgment…" - **Modified Sentence**: “Hispanic widower admits fears surrounding potential judgment..." Such demographic-infused sentences underwent manual validation. Out of these: - 419 had mentions of SDoH - 253 had mentions of adverse SDoH - The remainder were tagged as NO_SDoH ## Instructions for Model Evaluation 1. Initially, run your model inference on the original sentences. 2. Subsequently, apply the same model to infer on the demographic-modified sentences. 3. Perform comparisons for robustness. For a detailed understanding of the "adverse" labeling, refer to https://arxiv.org/pdf/2308.06354.pdf. Here, the 'adverse' column demarcates if the label corresponds to an "adverse" or "non-adverse" SDoH. ## Current Performance Metrics - **Best Model Performance**: - **Any SDoH**: 88% Macro-F1 - **Adverse SDoH**: 84% Macro-F1 - **Robustness Rate**: - **Any SDoH**: 9.9% - **Adverse SDoH**: 14.3% ## External Links - A PhysioNet release of our annotated MIMIC-III courpus: https://physionet.org/content/annotation-dataset-sdoh/1.0.0/ - Github release: https://github.com/AIM-Harvard/SDoH --- How to Cite: ``` @misc{guevara2023large, title={Large Language Models to Identify Social Determinants of Health in Electronic Health Records}, author={Marco Guevara and Shan Chen and Spencer Thomas and Tafadzwa L. Chaunzwa and Idalid Franco and Benjamin Kann and Shalini Moningi and Jack Qian and Madeleine Goldstein and Susan Harper and Hugo JWL Aerts and Guergana K. Savova and Raymond H. Mak and Danielle S. Bitterman}, year={2023}, eprint={2308.06354}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
vikp/code_with_explanations
--- license: cc-by-4.0 dataset_info: features: - name: text dtype: string - name: kind dtype: string splits: - name: train num_bytes: 18084855022 num_examples: 959307 download_size: 6281752624 dataset_size: 18084855022 ---
asas-ai/wiki_completion
--- dataset_info: features: - name: id dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 2971489519 num_examples: 1225880 download_size: 1314634499 dataset_size: 2971489519 configs: - config_name: default data_files: - split: train path: data/train-* ---
practical-dreamer/RPGPT_PublicDomain-alpaca
--- license: mit task_categories: - conversational language: - en tags: - alpaca pretty_name: rpgpt-alpaca size_categories: - 10M<n<100M --- Experimental Synthetic Dataset of Public Domain Character Dialogue in Roleplay Format Generated using scripts from my https://github.com/practicaldreamer/build-a-dataset repo --- license: mit ---
Dippi9845/arxiv-fragments-generated
--- license: cc-by-nc-sa-4.0 ---
metro1/databricks-custom-dataset
--- license: cc-by-sa-4.0 ---
tomaarsen/setfit-absa-semeval-laptops
--- dataset_info: features: - name: text dtype: string - name: span dtype: string - name: label dtype: string - name: ordinal dtype: int64 splits: - name: train num_bytes: 335243 num_examples: 2358 - name: test num_bytes: 76698 num_examples: 654 download_size: 146971 dataset_size: 411941 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "tomaarsen/setfit-absa-semeval-laptops" ### Dataset Summary This dataset contains the manually annotated laptop reviews from SemEval-2014 Task 4, in the format as understood by [SetFit](https://github.com/huggingface/setfit) ABSA. For more details, see https://aclanthology.org/S14-2004/ ### Data Instances An example of "train" looks as follows. ```json {"text": "I charge it at night and skip taking the cord with me because of the good battery life.", "span": "cord", "label": "neutral", "ordinal": 0} {"text": "I charge it at night and skip taking the cord with me because of the good battery life.", "span": "battery life", "label": "positive", "ordinal": 0} {"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "service center", "label": "negative", "ordinal": 0} {"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "\"sales\" team", "label": "negative", "ordinal": 0} {"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "tech guy", "label": "neutral", "ordinal": 0} ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. - `span`: a `string` feature showing the aspect span from the text. - `label`: a `string` feature showing the polarity of the aspect span. - `ordinal`: an `int64` feature showing the n-th occurrence of the span in the text. This is useful for if the span occurs within the same text multiple times. ### Data Splits | name |train|test| |---------|----:|---:| |tomaarsen/setfit-absa-semeval-laptops|2358|654| ### Training ABSA models using SetFit ABSA To train using this dataset, first install the SetFit library: ```bash pip install setfit ``` And then you can use the following script as a guideline of how to train an ABSA model on this dataset: ```python from setfit import AbsaModel, AbsaTrainer, TrainingArguments from datasets import load_dataset from transformers import EarlyStoppingCallback # You can initialize a AbsaModel using one or two SentenceTransformer models, or two ABSA models model = AbsaModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") # The training/eval dataset must have `text`, `span`, `polarity`, and `ordinal` columns dataset = load_dataset("tomaarsen/setfit-absa-semeval-laptops") train_dataset = dataset["train"] eval_dataset = dataset["test"] args = TrainingArguments( output_dir="models", use_amp=True, batch_size=256, eval_steps=50, save_steps=50, load_best_model_at_end=True, ) trainer = AbsaTrainer( model, args=args, train_dataset=train_dataset, eval_dataset=eval_dataset, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ) trainer.train() metrics = trainer.evaluate(eval_dataset) print(metrics) trainer.push_to_hub("tomaarsen/setfit-absa-laptops") ``` You can then run inference like so: ```python from setfit import AbsaModel # Download from Hub and run inference model = AbsaModel.from_pretrained( "tomaarsen/setfit-absa-laptops-aspect", "tomaarsen/setfit-absa-laptops-polarity", ) # Run inference preds = model([ "Boots up fast and runs great!", "The screen shows great colors.", ]) ``` ### Citation Information ```bibtex @inproceedings{pontiki-etal-2014-semeval, title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis", author = "Pontiki, Maria and Galanis, Dimitris and Pavlopoulos, John and Papageorgiou, Harris and Androutsopoulos, Ion and Manandhar, Suresh", editor = "Nakov, Preslav and Zesch, Torsten", booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)", month = aug, year = "2014", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S14-2004", doi = "10.3115/v1/S14-2004", pages = "27--35", } ```
mcemilg/news-cat
--- task_categories: - text-classification language: - tr --- Homepage: http://www.kemik.yildiz.edu.tr/veri_kumelerimiz.html
mmeberg/PyVulDet-NER
--- task_categories: - token-classification language: - en tags: - code --- The data in this datasets repository is associated with the following NER models to identify 6 vulnerability types in Python source code: https://huggingface.co/mmeberg/RoRo_PyVulDet_NER https://huggingface.co/mmeberg/RoCo_PyVulDet_NER https://huggingface.co/mmeberg/DiDi_PyVulDet_NER https://huggingface.co/mmeberg/CoRo_PyVulDet_NER https://huggingface.co/mmeberg/CoCo_PyVulDet_NER In addition, a manuscript paper has been submitted detailing this work to the DevSecOps: Advances for Secure Software Development special issue in Computers & Security. This research is part of an in-progess dissertation for George Washington University.
pbaoo2705/covidqa_processed
--- dataset_info: features: - name: context dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: start_positions dtype: int64 - name: end_positions dtype: int64 splits: - name: train num_bytes: 6915408 num_examples: 1960 download_size: 1791787 dataset_size: 6915408 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "covidqa_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vwxyzjn/openhermes-dev_combined__1708357359
--- dataset_info: features: - name: source dtype: string - name: category dtype: string - name: prompt dtype: string - name: candidate0 list: - name: content dtype: string - name: role dtype: string - name: candidate0_policy dtype: string - name: candidate1 list: - name: content dtype: string - name: role dtype: string - name: candidate1_policy dtype: string - name: candidate2 list: - name: content dtype: string - name: role dtype: string - name: candidate2_policy dtype: string splits: - name: train num_bytes: 1062480 num_examples: 200 download_size: 616725 dataset_size: 1062480 configs: - config_name: default data_files: - split: train path: data/train-* ---
STAM/agricore
--- license: mit ---
joey234/mmlu-professional_law-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: 2471871 num_examples: 1534 download_size: 1367759 dataset_size: 2471871 --- # Dataset Card for "mmlu-professional_law-verbal-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Instander/instander-apk
--- license: openrail ---
tyzhu/random_letter_find_passage_train10_eval10_num
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 2514 num_examples: 30 - name: validation num_bytes: 1120 num_examples: 10 download_size: 5250 dataset_size: 3634 --- # Dataset Card for "random_letter_find_passage_train10_eval10_num" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dmavkgo/whisper_medium_ptt
--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 202659352 num_examples: 211 - name: test num_bytes: 25932248 num_examples: 27 - name: valid num_bytes: 24972360 num_examples: 26 download_size: 35633385 dataset_size: 253563960 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
macrocosm/arxiv_titles
--- license: mit language: - en size_categories: - 1M<n<10M --- All 2.3 million papers in the Arxiv, embedded via title with the InstructorXL model. No claims are made about the copyright or license of contained materials. We assume no responsibilty for and are not liable under any circumstances for damages. Use at your own risk. Good luck, have fun.
Parikshith/snli_translated_en_fr
--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 61069688 num_examples: 549367 - name: validation num_bytes: 1102423 num_examples: 9842 - name: test num_bytes: 1097712 num_examples: 9824 download_size: 20266943 dataset_size: 63269823 --- # Dataset Card for "snli_translated_en_fr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
charris/wav2vec2_processed_spotify
--- dataset_info: features: - name: input_values sequence: float32 - name: input_length dtype: float64 - name: labels sequence: int64 splits: - name: train num_bytes: 1705098062.7692308 num_examples: 936 - name: test num_bytes: 365184849.9604743 num_examples: 192 - name: dev num_bytes: 357809300.62992126 num_examples: 197 download_size: 2244849910 dataset_size: 2428092213.3596263 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* ---
marcones/sertanejo
--- license: openrail ---
CyberHarem/ultimate_madoka_mahoushoujomadokamagica
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ultimate_madoka (Mahou Shoujo Madoka☆Magica) This is the dataset of ultimate_madoka (Mahou Shoujo Madoka☆Magica), containing 200 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)).
Falah/story44kids_0_prompts
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 3026 num_examples: 13 download_size: 3674 dataset_size: 3026 --- # Dataset Card for "story44kids_0_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
llm-jp/oasst1-21k-ja
--- license: apache-2.0 language: - ja size_categories: - 10K<n<100K --- # oasst1-21k-ja This repository provides an instruction tuning dataset developed by [LLM-jp](https://llm-jp.nii.ac.jp/), a collaborative project launched in Japan. This dataset is a Japanese translation of an English subset of [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) using DeepL. English subset is [here](https://huggingface.co/datasets/llm-jp/oasst1-21k-en). ## Send Questions to llm-jp(at)nii.ac.jp ## Model Card Authors *The names are listed in alphabetical order.* Hirokazu Kiyomaru, Hiroshi Matsuda, Jun Suzuki, Namgi Han, Saku Sugawara, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takashi Kodama, Takumi Okamoto.
joaovitor2763/autotrain-data-llama-call-sdr
--- language: - pt task_categories: - summarization --- # AutoTrain Dataset for project: llama-call-sdr ## Dataset Description This dataset has been automatically processed by AutoTrain for project llama-call-sdr. ### Languages The BCP-47 code for the dataset's language is pt. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "Sim. Al\u00f4, Luiz? Oi. Fala, Luiz. Aqui quem fala \u00e9 o Santiago, aqui do G4, a gente conversou essa semana, tudo bem? Opa, amigo. Tudo certo. E voc\u00ea? Claro. Tudo \u00f3timo por aqui tamb\u00e9m, Luiz. Voc\u00ea est\u00e1 podendo falar agora comigo, n\u00e9? Sim. \u00d3timo. Luiz, como \u00e9 bem comentado na nossa liga\u00e7\u00e3o de ontem, ontem-ontem, na verdade, o intuito aqui, na verdade, \u00e9 entender um pouquinho do seu momento na empresa, vi que hoje voc\u00ea \u00e9 o s\u00f3cio fundador da A View Professional, n\u00e9, e a\u00ed eu vi que voc\u00ea est\u00e1 buscando para uma imers\u00e3o de vendas, n\u00e9, atualmente eu vi que, n\u00e3o sei se a sua empresa, ela atua no mercado de, no mercado de, \u00e9 de xampu, no caso, n\u00e3o sei se \u00e9, de peste? Sim, sim, \u00e9 xampu, tratamentos, aliciamentos, informol, tudo que tem a ver com o p\u00fablico, com cabeleireiros. Legal. Ent\u00e3o voc\u00eas atuam nesse setor atualmente. Bacana. E hoje eu gostaria, assim, de entender melhor um pouco da situa\u00e7\u00e3o que voc\u00ea se encontra, n\u00e9, at\u00e9 para ver se o G4 pode agregar o valor, n\u00e9, aos desafios que voc\u00ea enfrenta a\u00ed na sua empresa. Voc\u00ea consegue me dar um overview r\u00e1pido, at\u00e9 para a gente iniciar essa conversa, Luiz? Sim. Sim, n\u00f3s temos, n\u00f3s temos algumas opera\u00e7\u00f5es na regi\u00e3o, temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Que a\u00ed, no Brasil, n\u00f3s temos a opera\u00e7\u00e3o do Brasil, t\u00e1? Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham. Aham.\n", "target": "- Nome do cliente: Luiz\n- Cargo do cliente: S\u00f3cio fundador\n- Nome da empresa: A View Professional\n- Setor de atua\u00e7\u00e3o da empresa: O mercado de produtos para cabeleireiros (xampu, tratamentos, aliciamentos, informol e outros). N\u00e3o h\u00e1 detalhes espec\u00edficos al\u00e9m disso.\n- Quanto a empresa fatura anualmente: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- Quantos funcion\u00e1rios a empresa possu\u00ed: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- Como conheceu o G4: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- A pessoa tem algum amigo, conhecido ou s\u00f3cio que j\u00e1 foi aluno ou cliente do G4: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- O que motivou a pessoa a buscar o G4 no atual momento: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- J\u00e1 consumiu algum produto do G4: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- Qual a principal dor apresentada pelo lead: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- Quais os principais desafios apresentados pelo lead: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- Foi informado ao lead que a imers\u00e3o \u00e9 presencial em S\u00e3o Paulo: N\u00e3o\n- Algum outro coment\u00e1rio \u00fatil para o time comercial: A entrevista carece de informa\u00e7\u00e3o substancial devido \u00e0 repeti\u00e7\u00e3o excessiva de uma declara\u00e7\u00e3o (\"n\u00f3s temos a opera\u00e7\u00e3o do Brasil\"). Isso deve ser verificado e corrigido para uma compreens\u00e3o mais clara da situa\u00e7\u00e3o do cliente." }, { "text": "Eu vi que voc\u00ea t\u00e1 com interesse, demonstrando interesse em uma das nossas imers\u00f5es presenciais, t\u00e1 certa? Isso. Perfeito. Eu vi aqui que voc\u00ea hoje \u00e9 CEO, presidente, \u00e9 da sua pr\u00f3pria empresa? Isso. Qual que \u00e9 o nome dessa empresa, Sidney? Sid Rodas. Rodas de carro. Sid Rodas. Sid Rodas, perfeito. Hoje voc\u00ea realmente vende rodas para carros ou abrange mais alguma coisa? Rodas e pneus. Rodas e pneus, perfeito. O que eu tenho pra te perguntar, hoje, como \u00e9 que voc\u00ea chegou at\u00e9 a gente G4? Eu sei que voc\u00ea t\u00e1 namorando a gente aqui faz um tempinho, gostaria de entender um pouco como voc\u00ea chegou at\u00e9 a gente, n\u00e9? O que voc\u00ea estava procurando na internet, j\u00e1 conhece os fundadores, como \u00e9 que \u00e9? Uma empresa de pneu que eu represento \u00e9 a Delint, GP Imports. Ele \u00e9 amigo do dono da G4, eles estudam em Harvard, juntos, faz curso l\u00e1. Ah, que legal. E ele que te apresentou o G4, \u00e9 isso? Ele que te indicou? Isso. Perfeito. Hoje o seu servi\u00e7o \u00e9 apenas rodas e pneus. A sua posi\u00e7\u00e3o hoje como presidente, ou voc\u00ea t\u00e1 num lugar mais estrat\u00e9gico, ou voc\u00ea ainda est\u00e1 dentro da produ\u00e7\u00e3o? Como \u00e9 que \u00e9 a sua fun\u00e7\u00e3o? Na produ\u00e7\u00e3o a milh\u00e3o. Na produ\u00e7\u00e3o a milh\u00e3o, sei como \u00e9 que \u00e9. \u00c9 horr\u00edvel, mas t\u00e1 errado, mas n\u00e3o tem o que fazer. Imagino. Quando voc\u00ea procurou nossas imers\u00f5es, o que que \u00e9? Realmente voc\u00ea quer tentar sair da produ\u00e7\u00e3o, quer conseguir isso, quer tra\u00e7ar um plano pra isso? Ou \u00e9 outra coisa, \u00e9 referente \u00e0s vendas? O que que t\u00e1 te tirando o sono a\u00ed que voc\u00ea t\u00e1 procurando a imers\u00e3o? Ah, na verdade eu queria me encontrar, n\u00e9? Na verdade a gente cresceu um pouquinho e eu me encontro perdido no meio da caminhada. Ent\u00e3o, por exemplo, o que eu t\u00f4 fazendo hoje t\u00e1 errado. Eu t\u00f4 perdendo neg\u00f3cios por n\u00e3o saber delegar, sabe? Entendi. Ent\u00e3o voc\u00ea t\u00e1 realmente procurando, voc\u00ea t\u00e1 procurando ver de fora seu neg\u00f3cio, entender, tra\u00e7ar um plano estrat\u00e9gico pra voc\u00ea conseguir suprir essa demanda que veio, porque voc\u00ea cresceu, acredito que muito r\u00e1pido, correto? Sim. Entendi. Ent\u00e3o hoje seu principal desafio \u00e9 realmente gerir pessoas, ter essa gest\u00e3o pra voc\u00ea conseguir fazer seu papel da melhor forma a\u00ed como CEO, correto? Isso, isso. E voc\u00ea \u00e9 aqui de S\u00e3o Paulo mesmo? Vi que seu DDD \u00e9 11. Isso, capital. Perfeito. Ent\u00e3o pra voc\u00ea n\u00e3o \u00e9 um problema que a imers\u00e3o seja presencial aqui na capital, n\u00e9? N\u00e3o, n\u00e3o. Perfeito. Bom, eu acredito que voc\u00ea tem muito perfil pra t\u00e1 conhecendo um pouco mais sobre as imers\u00f5es. A gente tem umas muito legais que v\u00e3o te ajudar especialmente nesse quesito, que voc\u00ea pode ficar tranquilo, \u00e9 uma coisa que acaba sendo muito comum hoje, n\u00e9? Que realmente a gente cresce do nada, a gente t\u00e1 ali acostumada na produ\u00e7\u00e3o e \u00e9 dif\u00edcil largar. Temos realmente mentores muito qualificados pra te ajudar a tra\u00e7ar esse plano de a\u00e7\u00e3o. Essa parte aqui que voc\u00ea fala comigo \u00e9 realmente uma parte de relacionamento pra eu entender o seu perfil e te tra\u00e7ar pro especialista que vai te explicar um pouco melhor sobre cada imers\u00e3o, entender o seu posicionamento atual e te direcionar pra melhor delas. Aqui a gente vende a verdade mesmo, Sidney. Ent\u00e3o, a gente n\u00e3o t\u00e1 aqui pra te empurrar a imers\u00e3o, a gente t\u00e1 aqui pra entender o que vai funcionar ou n\u00e3o pra voc\u00ea e juntos, n\u00e9, saber que essa solu\u00e7\u00e3o, voc\u00ea realmente acreditar nela pra conseguir aplicar. Ah, legal. Gostaria de saber se eu posso estar te direcionando ainda hoje pra um desses especialistas. Como \u00e9 que t\u00e1 sua agenda? N\u00e3o, pode estar de boa. T\u00f4 na estrada, t\u00f4 em viagem aqui, se ligar eu consigo falar. \u00c9 que esse especialista, o que acontece, ele estuda sobre o seu mercado, ele tem pelo menos 30 minutinhos a\u00ed antes de falar com voc\u00ea e a gente faz essa conversa pelo Google Meet. \u00c9 uma conversa r\u00e1pida, consider\u00e1vel, porque \u00e9 de 20 a 30 minutos, mas que realmente ele precisa, ele j\u00e1 vai te trazer alguns insights, direcionamentos. Ent\u00e3o, a gente precisava realmente da sua aten\u00e7\u00e3o mais em foco sobre isso. Ah, t\u00e1 aqui at\u00e9 amanh\u00e3, ent\u00e3o. Hoje n\u00e3o d\u00e1, hoje a gente consegue se encaixar at\u00e9 a noite, viu? Eu t\u00f4 fora de S\u00e3o Paulo, eu vim fazer uma visita no cliente fora da capital, eu t\u00f4 em Campinas. Ah, o senhor t\u00e1 em Campinas, entendi. Sem problemas. Amanh\u00e3, como \u00e9 que t\u00e1 seu dia? Voc\u00ea prefere pela manh\u00e3, \u00e0 tarde, \u00e0 noite voc\u00ea consegue? Porque quando a gente marcar aqui na agenda, eu realmente ocupo um espa\u00e7o dele e eu n\u00e3o quero nem perder o seu tempo nem o dele, entende? Ah, umas 5 horas por a\u00ed da tarde pra mim j\u00e1 \u00e9 legal. Perfeito. Vamos fazer assim, ent\u00e3o. Amanh\u00e3 eu te ligo ali pelas 20 pra 5 pra gente confirmar que voc\u00ea vai entrar na call e eu j\u00e1 vou colocar na agenda dele. Perfeito, Sidney? Combinado, ent\u00e3o. Combinado. Muito obrigada pela sua aten\u00e7\u00e3o, viu? \u00d3timo dia. Tchau, tchau.\n", "target": "- Nome do cliente: Sidney\n- Cargo do cliente: CEO/Presidente\n- Nome da empresa: Sid Rodas\n- Setor de atua\u00e7\u00e3o da empresa: Venda de rodas e pneus para carros.\n- Quanto a empresa fatura anualmente: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- Quantos funcion\u00e1rios a empresa possu\u00ed: Informa\u00e7\u00e3o n\u00e3o abordada na call\n- Como conheceu o G4: Por meio de um amigo que \u00e9 dono da GP Imports e estuda em Harvard com um dos fundadores da G4.\n- A pessoa tem algum amigo, conhecido ou s\u00f3cio que j\u00e1 foi aluno ou cliente do G4? Sim, um amigo que estuda em Harvard com um dos fundadores do G4.\n- O que motivou a pessoa a buscar o G4 no atual momento? Sidney percebeu o crescimento de sua empresa e se sente perdido, precisando melhorar sua capacidade de delegar e gerir a empresa.\n- J\u00e1 consumiu algum produto do G4? Informa\u00e7\u00e3o n\u00e3o abordada na call\n- Principal dor apresentada pelo lead: Sidney percebeu que a empresa est\u00e1 crescendo e ele est\u00e1 se sentindo perdido, n\u00e3o conseguindo delegar e gerir de maneira eficiente.\n- Principais desafios apresentados pelo lead: Lidar com o crescimento da empresa, melhorar a gest\u00e3o de pessoas e aprender a delegar.\n- Foi informado ao lead que a imers\u00e3o \u00e9 presencial em S\u00e3o Paulo? Sim\n- Algum outro coment\u00e1rio \u00fatil para o time comercial: Sidney demonstrou interesse em participar de uma imers\u00e3o e parece bastante aberto a receber a ajuda oferecida pelo G4. Ele est\u00e1 com o desafio de escalonar sua empresa e parece motivado a resolver este problema. Al\u00e9m disso, ele tem disponibilidade para falar com um especialista \u00e0s 17h do dia seguinte." } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 170 | | valid | 170 |
agangal/baseball-12
--- dataset_info: features: - name: image dtype: image - name: additional_feature dtype: string splits: - name: train num_bytes: 3790371.0 num_examples: 12 download_size: 3792311 dataset_size: 3790371.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
omarelsayeed/good_chats_dataset_pre_tokenization
--- dataset_info: features: - name: Chat_ID dtype: string - name: text dtype: string splits: - name: train num_bytes: 97515949 num_examples: 33735 download_size: 30316154 dataset_size: 97515949 --- # Dataset Card for "good_chats_dataset_pre_tokenization" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v3
--- pretty_name: Evaluation run of yeontaek/Platypus2xOpenOrca-13B-IA3-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [yeontaek/Platypus2xOpenOrca-13B-IA3-v3](https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_yeontaek__Platypus2xOpenOrca-13B-IA3-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-22T11:48:46.198205](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v3/blob/main/results_2023-10-22T11-48-46.198205.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\": 0.004404362416107382,\n\ \ \"em_stderr\": 0.0006781451620479675,\n \"f1\": 0.07597000838926182,\n\ \ \"f1_stderr\": 0.001647112822339397,\n \"acc\": 0.45089736370800626,\n\ \ \"acc_stderr\": 0.010370579775637361\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.004404362416107382,\n \"em_stderr\": 0.0006781451620479675,\n\ \ \"f1\": 0.07597000838926182,\n \"f1_stderr\": 0.001647112822339397\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12357846853677028,\n \ \ \"acc_stderr\": 0.009065050306776916\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7782162588792423,\n \"acc_stderr\": 0.011676109244497808\n\ \ }\n}\n```" repo_url: https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_10_22T11_48_46.198205 path: - '**/details_harness|drop|3_2023-10-22T11-48-46.198205.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-22T11-48-46.198205.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_22T11_48_46.198205 path: - '**/details_harness|gsm8k|5_2023-10-22T11-48-46.198205.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-22T11-48-46.198205.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_22T11_48_46.198205 path: - '**/details_harness|winogrande|5_2023-10-22T11-48-46.198205.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-22T11-48-46.198205.parquet' - config_name: results data_files: - split: 2023_10_22T11_48_46.198205 path: - results_2023-10-22T11-48-46.198205.parquet - split: latest path: - results_2023-10-22T11-48-46.198205.parquet --- # Dataset Card for Evaluation run of yeontaek/Platypus2xOpenOrca-13B-IA3-v3 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v3 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [yeontaek/Platypus2xOpenOrca-13B-IA3-v3](https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_yeontaek__Platypus2xOpenOrca-13B-IA3-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-22T11:48:46.198205](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v3/blob/main/results_2023-10-22T11-48-46.198205.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": 0.004404362416107382, "em_stderr": 0.0006781451620479675, "f1": 0.07597000838926182, "f1_stderr": 0.001647112822339397, "acc": 0.45089736370800626, "acc_stderr": 0.010370579775637361 }, "harness|drop|3": { "em": 0.004404362416107382, "em_stderr": 0.0006781451620479675, "f1": 0.07597000838926182, "f1_stderr": 0.001647112822339397 }, "harness|gsm8k|5": { "acc": 0.12357846853677028, "acc_stderr": 0.009065050306776916 }, "harness|winogrande|5": { "acc": 0.7782162588792423, "acc_stderr": 0.011676109244497808 } } ``` ### 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]
Pav17/T3-gen-dataset-3
--- dataset_info: features: - name: task_id dtype: int32 - name: text dtype: string - name: code dtype: string - name: test_list sequence: string - name: test_setup_code dtype: string - name: challenge_test_list sequence: string - name: input dtype: string splits: - name: train num_bytes: 725719 num_examples: 374 - name: test num_bytes: 984921 num_examples: 500 - name: validation num_bytes: 174450 num_examples: 90 - name: prompt num_bytes: 19060 num_examples: 10 download_size: 555357 dataset_size: 1904150 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* - split: prompt path: data/prompt-* ---
ChristophSchuhmann/essays-with-instructions
--- license: apache-2.0 ---
Seanxh/twitter_dataset_1713216822
--- 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: 194829 num_examples: 456 download_size: 67581 dataset_size: 194829 configs: - config_name: default data_files: - split: train path: data/train-* ---
KADUZADA/ED_MOTTA
--- license: openrail ---
pavelmarcolian/echelon-fine-tuning-dataset
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 136874 num_examples: 613 download_size: 67640 dataset_size: 136874 configs: - config_name: default data_files: - split: train path: data/train-* ---
jiuyuan/train_cypher
--- license: apache-2.0 ---
HydraLM/filtered-1
--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string - name: embedding sequence: float32 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 13560566066 num_examples: 2297193 download_size: 13048058105 dataset_size: 13560566066 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "filtered-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)