Datasets:
davanstrien
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summary-test

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sartajbhuvaji/self-driving-GTA-V
--- license: mit task_categories: - image-classification tags: - self driving - GTA - GTA V - driving size_categories: - 1M<n<10M source_datasets: - original configs: - config_name: default data_files: - split: mini path: training_data_count_mini.csv - split: TrainingData_1 path: training_data_count_001-100.csv - split: TrainingData_2 path: training_data_count_101-200.csv --- # Self Driving GTA V Dataset ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6354695712edd0ed5dc46b04/AwMYg8s3uLaPLyvUrIf8w.png) # Dataset Varients - Mini : [Link](https://huggingface.co/datasets/sartajbhuvaji/self-driving-GTA-V/tree/main/mini) - Training Data(1-100) : [Link](https://huggingface.co/datasets/sartajbhuvaji/self-driving-GTA-V/tree/main/Training%20Data(1-100)) - Training Data(101-200) : [Link](https://huggingface.co/datasets/sartajbhuvaji/self-driving-GTA-V/tree/main/Training%20Data(101-200)) ### Info - Image Resolution : 270, 480 - Mode : RGB - Dimension : (270, 480, 3) - File Count : 100 - Size : 1.81 GB/file - Total Data Size : 362 GB - Total Frames : 1 Million ### Data Set sizes #### Mini : - Folder Name : mini - Files : 01 - Total Size : 1.81 GB - Total Frames : 5000 #### First Half - Folder Name : Training Data(1-100) - Files : 100 - Total Size : 181 GB - Total Frames : 500,000 #### Second Half - Folder Name : Training Data(101-200) - Files : 100 - Total Size : 181 GB - Total Frames : 500,000 ### Data Count #### Mini ``` 'W': [1, 0, 0, 0, 0, 0, 0, 0, 0] : 3627 'S': [0, 1, 0, 0, 0, 0, 0, 0, 0] : 50 'A': [0, 0, 1, 0, 0, 0, 0, 0, 0] : 104 'D': [0, 0, 0, 1, 0, 0, 0, 0, 0] : 106 'WA': [0, 0, 0, 0, 1, 0, 0, 0, 0] : 364 'WD': [0, 0, 0, 0, 0, 1, 0, 0, 0] : 416 'SA': [0, 0, 0, 0, 0, 0, 1, 0, 0] : 35 'SD': [0, 0, 0, 0, 0, 0, 0, 1, 0] : 47 'NK': [0, 0, 0, 0, 0, 0, 0, 0, 1] : 248 NONE : 3 ``` #### First Half (Data Count (1-100)) ``` 'W': [1, 0, 0, 0, 0, 0, 0, 0, 0] : 353725 'S': [0, 1, 0, 0, 0, 0, 0, 0, 0] : 2243 'A': [0, 0, 1, 0, 0, 0, 0, 0, 0] : 14303 'D': [0, 0, 0, 1, 0, 0, 0, 0, 0] : 13114 'WA': [0, 0, 0, 0, 1, 0, 0, 0, 0] : 30877 'WD': [0, 0, 0, 0, 0, 1, 0, 0, 0] : 29837 'SA': [0, 0, 0, 0, 0, 0, 1, 0, 0] : 1952 'SD': [0, 0, 0, 0, 0, 0, 0, 1, 0] : 1451 'NK': [0, 0, 0, 0, 0, 0, 0, 0, 1] : 52256 NONE : 242 ``` #### Second Half (Data Count (101-200)) ``` 'W': [1, 0, 0, 0, 0, 0, 0, 0, 0] : 359025 'S': [0, 1, 0, 0, 0, 0, 0, 0, 0] : 2834 'A': [0, 0, 1, 0, 0, 0, 0, 0, 0] : 11025 'D': [0, 0, 0, 1, 0, 0, 0, 0, 0] : 9639 'WA': [0, 0, 0, 0, 1, 0, 0, 0, 0] : 31896 'WD': [0, 0, 0, 0, 0, 1, 0, 0, 0] : 29756 'SA': [0, 0, 0, 0, 0, 0, 1, 0, 0] : 1742 'SD': [0, 0, 0, 0, 0, 0, 0, 1, 0] : 2461 'NK': [0, 0, 0, 0, 0, 0, 0, 0, 1] : 51313 NONE : 309 ``` ### Graphics Details - Original Resolution : 800 x 600 - Aspect Ratio : 16:10 - All Video Settings : Low ### Camera Details - Camera : Hood Cam - Vehical Camera Height : Low - First Person Vehical Auto-Center : On - First Person Head Bobbing : Off ### Other Details - Vehical : Michael's Car - Vehical Mods : All Max - Cv2 Mask : None - Way Point : Enabled/Following - Weather Conditions : Mostly Sunny - Time of Day : Day, Night - Rain : Some ### Note - Remove `NONE` while processing the data - Use the `mini` dataset for initial setup and testing - Check `training_data_count_001-100.csv` & `training_data_count_101-200.csv` for detailed count - Check `training_data_stats.py` for more info ### Inspired From - Sentdex - [Youtube: Python Plays: Grand Theft Auto V](https://youtube.com/playlist?list=PLQVvvaa0QuDeETZEOy4VdocT7TOjfSA8a&si=M5Pt-O97yvWgZMQE)
nyuuzyou/wb-products
--- annotations_creators: - crowdsourced language: - ru language_creators: - crowdsourced license: - cc0-1.0 multilinguality: - monolingual pretty_name: Wildberries products size_categories: - 100M<n<1B source_datasets: - original task_categories: - text-generation task_ids: - language-modeling --- # Dataset Card for Wildberries products ### Dataset Summary This dataset was scraped from product pages on the Russian marketplace [Wildberries](https://www.wildberries.ru). It includes all information from the product card and metadata from the API, excluding image URLs. The dataset was collected by processing approximately 160 million products out of a potential 230 million, starting from the first product. Data collection had to be stopped due to serious rate limits that prevented further progress. The data is in zstd archives containing jsonl files. Each archive contains data from a specific Wildberries data server identified by a basket server number. ### Languages The dataset is mostly in Russian, but there may be other languages present. ## Dataset Structure ### Data Fields This dataset includes the following fields: - `imt_id`: Identifier for the item (integer) - `nm_id`: Numeric identifier associated with the item (integer) - `imt_name`: Name of the product (string) - `subj_name`: Subject name (string) - `subj_root_name`: Root subject name (string) - `nm_colors_names`: Colors names (string, may be empty) - `vendor_code`: Vendor code (string) - `description`: Description of the product (string, may be empty) - `brand_name`: Name of the brand (string) ### Data Splits All examples are in the train split, there is no validation split. ## Additional Information ### License This dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can: * Use it for any purpose, including commercial projects. * Modify it however you like. * Distribute it without asking permission. No attribution is required, but it's always appreciated! CC0 license: https://creativecommons.org/publicdomain/zero/1.0/deed.en To learn more about CC0, visit the Creative Commons website: https://creativecommons.org/publicdomain/zero/1.0/ ### Dataset Curators - [nyuuzyou](https://ducks.party)
davide221/verilog-instruct-deepseek-60k
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 118916285 num_examples: 60199 download_size: 37374425 dataset_size: 118916285 configs: - config_name: default data_files: - split: train path: data/train-* ---
michelcarroll/llama2-earnings-stock-prediction-fine-tune-v2
--- dataset_info: features: - name: completion dtype: string - name: label dtype: string splits: - name: train num_bytes: 87920323 num_examples: 111140 - name: development num_bytes: 26603449 num_examples: 33284 - name: test num_bytes: 840735 num_examples: 1000 download_size: 47167270 dataset_size: 115364507 configs: - config_name: default data_files: - split: train path: data/train-* - split: development path: data/development-* - split: test path: data/test-* ---
Multimodal-Fatima/VQAv2_minival_validation
--- dataset_info: features: - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: id_image dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: image dtype: image - name: clip_tags_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_clip_ViT_L_14 list: - name: attribute dtype: string - name: box sequence: float64 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption list: - name: attribute dtype: string - name: box sequence: float64 - name: caption dtype: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: id dtype: int64 - name: DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module list: - name: attribute dtype: string - name: box sequence: float64 - name: caption dtype: string - name: captions_module sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_without_filtering list: - name: attribute dtype: string - name: box sequence: float64 - name: caption dtype: string - name: captions_module sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - name: caption dtype: string - name: captions_module sequence: string - name: captions_module_filter sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: clip_tags_LAION_ViT_H_14_2B sequence: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B sequence: string - name: Attributes_ViT_L_14_descriptors_text_davinci_003_full sequence: string - name: clip_tags_ViT_L_14_wo_openai sequence: string - name: clip_tags_ViT_L_14_with_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_with_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_with_openai sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full sequence: string - name: clip_tags_ViT_B_16_with_openai sequence: string - name: blip_caption_beam_5_Salesforce_blip2_flan_t5_xxl dtype: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_ list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_all_patches sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_all_patches sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: blip_caption_Salesforce_blip_image_captioning_large_intensive sequence: string - name: blip_caption_Salesforce_blip_image_captioning_base_intensive sequence: string splits: - name: validation num_bytes: 10757838822.0 num_examples: 25994 download_size: 2788131849 dataset_size: 10757838822.0 --- # Dataset Card for "VQAv2_minival_validation_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
codeparrot/github-jupyter-parsed
--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - muonolingual size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling --- # GitHub Jupyter Dataset ## Dataset Description This is a parsed and preprocessed version of [GitHub-Jupyter Dataset](https://huggingface.co/datasets/codeparrot/github-jupyter), a dataset extracted from Jupyter Notebooks on BigQuery. We only keep markdown and python cells and convert the markdown to text. Some heuristics are also applied to filter notebooks with little data and very long or very short cells. ## Licenses Each example has the license of its associated repository. There are in total 15 licenses: ```python [ 'mit', 'apache-2.0', 'gpl-3.0', 'gpl-2.0', 'bsd-3-clause', 'agpl-3.0', 'lgpl-3.0', 'lgpl-2.1', 'bsd-2-clause', 'cc0-1.0', 'epl-1.0', 'mpl-2.0', 'unlicense', 'isc', 'artistic-2.0' ] ```
open-llm-leaderboard/details_vitruv__vitruv_1
--- pretty_name: Evaluation run of vitruv/vitruv_1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [vitruv/vitruv_1](https://huggingface.co/vitruv/vitruv_1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_vitruv__vitruv_1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-06T02:40:06.932046](https://huggingface.co/datasets/open-llm-leaderboard/details_vitruv__vitruv_1/blob/main/results_2024-03-06T02-40-06.932046.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\": 0.48098814582864774,\n\ \ \"acc_stderr\": 0.034444629168415404,\n \"acc_norm\": 0.48708235528126465,\n\ \ \"acc_norm_stderr\": 0.03523822694795442,\n \"mc1\": 0.25458996328029376,\n\ \ \"mc1_stderr\": 0.01525011707915648,\n \"mc2\": 0.4122590137037498,\n\ \ \"mc2_stderr\": 0.014277193708018924\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4726962457337884,\n \"acc_stderr\": 0.014589589101985994,\n\ \ \"acc_norm\": 0.4991467576791809,\n \"acc_norm_stderr\": 0.014611369529813276\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5605457080262896,\n\ \ \"acc_stderr\": 0.0049530634047914536,\n \"acc_norm\": 0.7605058753236407,\n\ \ \"acc_norm_stderr\": 0.004259025448541507\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5333333333333333,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.5333333333333333,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.40789473684210525,\n \"acc_stderr\": 0.039993097127774734,\n\ \ \"acc_norm\": 0.40789473684210525,\n \"acc_norm_stderr\": 0.039993097127774734\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.539622641509434,\n \"acc_stderr\": 0.030676096599389177,\n\ \ \"acc_norm\": 0.539622641509434,\n \"acc_norm_stderr\": 0.030676096599389177\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5416666666666666,\n\ \ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n\ \ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \"acc_norm\"\ : 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.47398843930635837,\n\ \ \"acc_stderr\": 0.03807301726504511,\n \"acc_norm\": 0.47398843930635837,\n\ \ \"acc_norm_stderr\": 0.03807301726504511\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.43829787234042555,\n \"acc_stderr\": 0.03243618636108101,\n\ \ \"acc_norm\": 0.43829787234042555,\n \"acc_norm_stderr\": 0.03243618636108101\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3157894736842105,\n\ \ \"acc_stderr\": 0.043727482902780064,\n \"acc_norm\": 0.3157894736842105,\n\ \ \"acc_norm_stderr\": 0.043727482902780064\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4206896551724138,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.4206896551724138,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3439153439153439,\n \"acc_stderr\": 0.024464426625596437,\n \"\ acc_norm\": 0.3439153439153439,\n \"acc_norm_stderr\": 0.024464426625596437\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\ \ \"acc_stderr\": 0.040735243221471255,\n \"acc_norm\": 0.29365079365079366,\n\ \ \"acc_norm_stderr\": 0.040735243221471255\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5774193548387097,\n\ \ \"acc_stderr\": 0.02810096472427264,\n \"acc_norm\": 0.5774193548387097,\n\ \ \"acc_norm_stderr\": 0.02810096472427264\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3448275862068966,\n \"acc_stderr\": 0.03344283744280458,\n\ \ \"acc_norm\": 0.3448275862068966,\n \"acc_norm_stderr\": 0.03344283744280458\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6181818181818182,\n \"acc_stderr\": 0.03793713171165634,\n\ \ \"acc_norm\": 0.6181818181818182,\n \"acc_norm_stderr\": 0.03793713171165634\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6666666666666666,\n \"acc_stderr\": 0.033586181457325226,\n \"\ acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.033586181457325226\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.6632124352331606,\n \"acc_stderr\": 0.03410780251836184,\n\ \ \"acc_norm\": 0.6632124352331606,\n \"acc_norm_stderr\": 0.03410780251836184\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.43333333333333335,\n \"acc_stderr\": 0.025124653525885117,\n\ \ \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.025124653525885117\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2777777777777778,\n \"acc_stderr\": 0.02730914058823018,\n \ \ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.02730914058823018\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.4327731092436975,\n \"acc_stderr\": 0.03218358107742613,\n \ \ \"acc_norm\": 0.4327731092436975,\n \"acc_norm_stderr\": 0.03218358107742613\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6091743119266055,\n \"acc_stderr\": 0.02092005834611106,\n \"\ acc_norm\": 0.6091743119266055,\n \"acc_norm_stderr\": 0.02092005834611106\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.3611111111111111,\n \"acc_stderr\": 0.032757734861009996,\n \"\ acc_norm\": 0.3611111111111111,\n \"acc_norm_stderr\": 0.032757734861009996\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6127450980392157,\n \"acc_stderr\": 0.03418931233833344,\n \"\ acc_norm\": 0.6127450980392157,\n \"acc_norm_stderr\": 0.03418931233833344\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6329113924050633,\n \"acc_stderr\": 0.031376240725616185,\n \ \ \"acc_norm\": 0.6329113924050633,\n \"acc_norm_stderr\": 0.031376240725616185\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5874439461883408,\n\ \ \"acc_stderr\": 0.03304062175449297,\n \"acc_norm\": 0.5874439461883408,\n\ \ \"acc_norm_stderr\": 0.03304062175449297\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.4732824427480916,\n \"acc_stderr\": 0.04379024936553894,\n\ \ \"acc_norm\": 0.4732824427480916,\n \"acc_norm_stderr\": 0.04379024936553894\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6115702479338843,\n \"acc_stderr\": 0.044492703500683836,\n \"\ acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.044492703500683836\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5833333333333334,\n\ \ \"acc_stderr\": 0.04766075165356461,\n \"acc_norm\": 0.5833333333333334,\n\ \ \"acc_norm_stderr\": 0.04766075165356461\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.50920245398773,\n \"acc_stderr\": 0.03927705600787443,\n\ \ \"acc_norm\": 0.50920245398773,\n \"acc_norm_stderr\": 0.03927705600787443\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.04689765937278135,\n\ \ \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.04689765937278135\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7692307692307693,\n\ \ \"acc_stderr\": 0.027601921381417593,\n \"acc_norm\": 0.7692307692307693,\n\ \ \"acc_norm_stderr\": 0.027601921381417593\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6883780332056194,\n\ \ \"acc_stderr\": 0.016562433867284176,\n \"acc_norm\": 0.6883780332056194,\n\ \ \"acc_norm_stderr\": 0.016562433867284176\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.48554913294797686,\n \"acc_stderr\": 0.026907849856282542,\n\ \ \"acc_norm\": 0.48554913294797686,\n \"acc_norm_stderr\": 0.026907849856282542\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25921787709497207,\n\ \ \"acc_stderr\": 0.014655780837497731,\n \"acc_norm\": 0.25921787709497207,\n\ \ \"acc_norm_stderr\": 0.014655780837497731\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5032679738562091,\n \"acc_stderr\": 0.028629305194003543,\n\ \ \"acc_norm\": 0.5032679738562091,\n \"acc_norm_stderr\": 0.028629305194003543\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5819935691318328,\n\ \ \"acc_stderr\": 0.028013651891995072,\n \"acc_norm\": 0.5819935691318328,\n\ \ \"acc_norm_stderr\": 0.028013651891995072\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5432098765432098,\n \"acc_stderr\": 0.027716661650194038,\n\ \ \"acc_norm\": 0.5432098765432098,\n \"acc_norm_stderr\": 0.027716661650194038\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3723404255319149,\n \"acc_stderr\": 0.028838921471251455,\n \ \ \"acc_norm\": 0.3723404255319149,\n \"acc_norm_stderr\": 0.028838921471251455\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.34419817470664926,\n\ \ \"acc_stderr\": 0.01213443374100257,\n \"acc_norm\": 0.34419817470664926,\n\ \ \"acc_norm_stderr\": 0.01213443374100257\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.02952009569768776,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.02952009569768776\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.46078431372549017,\n \"acc_stderr\": 0.02016552331390791,\n \ \ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.02016552331390791\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5545454545454546,\n\ \ \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.5545454545454546,\n\ \ \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5387755102040817,\n \"acc_stderr\": 0.03191282052669278,\n\ \ \"acc_norm\": 0.5387755102040817,\n \"acc_norm_stderr\": 0.03191282052669278\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6069651741293532,\n\ \ \"acc_stderr\": 0.0345368246603156,\n \"acc_norm\": 0.6069651741293532,\n\ \ \"acc_norm_stderr\": 0.0345368246603156\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.41566265060240964,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.41566265060240964,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.6549707602339181,\n \"acc_stderr\": 0.036459813773888065,\n\ \ \"acc_norm\": 0.6549707602339181,\n \"acc_norm_stderr\": 0.036459813773888065\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.25458996328029376,\n\ \ \"mc1_stderr\": 0.01525011707915648,\n \"mc2\": 0.4122590137037498,\n\ \ \"mc2_stderr\": 0.014277193708018924\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7158642462509865,\n \"acc_stderr\": 0.012675392786772727\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11296436694465505,\n \ \ \"acc_stderr\": 0.008719339028833067\n }\n}\n```" repo_url: https://huggingface.co/vitruv/vitruv_1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|arc:challenge|25_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-06T02-40-06.932046.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|gsm8k|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hellaswag|10_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-06T02-40-06.932046.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-management|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-06T02-40-06.932046.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|truthfulqa:mc|0_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-06T02-40-06.932046.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_06T02_40_06.932046 path: - '**/details_harness|winogrande|5_2024-03-06T02-40-06.932046.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-06T02-40-06.932046.parquet' - config_name: results data_files: - split: 2024_03_06T02_40_06.932046 path: - results_2024-03-06T02-40-06.932046.parquet - split: latest path: - results_2024-03-06T02-40-06.932046.parquet --- # Dataset Card for Evaluation run of vitruv/vitruv_1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [vitruv/vitruv_1](https://huggingface.co/vitruv/vitruv_1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_vitruv__vitruv_1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-06T02:40:06.932046](https://huggingface.co/datasets/open-llm-leaderboard/details_vitruv__vitruv_1/blob/main/results_2024-03-06T02-40-06.932046.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": 0.48098814582864774, "acc_stderr": 0.034444629168415404, "acc_norm": 0.48708235528126465, "acc_norm_stderr": 0.03523822694795442, "mc1": 0.25458996328029376, "mc1_stderr": 0.01525011707915648, "mc2": 0.4122590137037498, "mc2_stderr": 0.014277193708018924 }, "harness|arc:challenge|25": { "acc": 0.4726962457337884, "acc_stderr": 0.014589589101985994, "acc_norm": 0.4991467576791809, "acc_norm_stderr": 0.014611369529813276 }, "harness|hellaswag|10": { "acc": 0.5605457080262896, "acc_stderr": 0.0049530634047914536, "acc_norm": 0.7605058753236407, "acc_norm_stderr": 0.004259025448541507 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5333333333333333, "acc_stderr": 0.043097329010363554, "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.40789473684210525, "acc_stderr": 0.039993097127774734, "acc_norm": 0.40789473684210525, "acc_norm_stderr": 0.039993097127774734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.539622641509434, "acc_stderr": 0.030676096599389177, "acc_norm": 0.539622641509434, "acc_norm_stderr": 0.030676096599389177 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5416666666666666, "acc_stderr": 0.04166666666666665, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.04166666666666665 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.23, "acc_stderr": 0.042295258468165065, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.37, "acc_stderr": 0.04852365870939098, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939098 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.47398843930635837, "acc_stderr": 0.03807301726504511, "acc_norm": 0.47398843930635837, "acc_norm_stderr": 0.03807301726504511 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364395, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364395 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.43829787234042555, "acc_stderr": 0.03243618636108101, "acc_norm": 0.43829787234042555, "acc_norm_stderr": 0.03243618636108101 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3157894736842105, "acc_stderr": 0.043727482902780064, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.043727482902780064 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4206896551724138, "acc_stderr": 0.0411391498118926, "acc_norm": 0.4206896551724138, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3439153439153439, "acc_stderr": 0.024464426625596437, "acc_norm": 0.3439153439153439, "acc_norm_stderr": 0.024464426625596437 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.29365079365079366, "acc_stderr": 0.040735243221471255, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.040735243221471255 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5774193548387097, "acc_stderr": 0.02810096472427264, "acc_norm": 0.5774193548387097, "acc_norm_stderr": 0.02810096472427264 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3448275862068966, "acc_stderr": 0.03344283744280458, "acc_norm": 0.3448275862068966, "acc_norm_stderr": 0.03344283744280458 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6181818181818182, "acc_stderr": 0.03793713171165634, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.03793713171165634 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6666666666666666, "acc_stderr": 0.033586181457325226, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.033586181457325226 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6632124352331606, "acc_stderr": 0.03410780251836184, "acc_norm": 0.6632124352331606, "acc_norm_stderr": 0.03410780251836184 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.43333333333333335, "acc_stderr": 0.025124653525885117, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.025124653525885117 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2777777777777778, "acc_stderr": 0.02730914058823018, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.02730914058823018 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.4327731092436975, "acc_stderr": 0.03218358107742613, "acc_norm": 0.4327731092436975, "acc_norm_stderr": 0.03218358107742613 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6091743119266055, "acc_stderr": 0.02092005834611106, "acc_norm": 0.6091743119266055, "acc_norm_stderr": 0.02092005834611106 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3611111111111111, "acc_stderr": 0.032757734861009996, "acc_norm": 0.3611111111111111, "acc_norm_stderr": 0.032757734861009996 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6127450980392157, "acc_stderr": 0.03418931233833344, "acc_norm": 0.6127450980392157, "acc_norm_stderr": 0.03418931233833344 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6329113924050633, "acc_stderr": 0.031376240725616185, "acc_norm": 0.6329113924050633, "acc_norm_stderr": 0.031376240725616185 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5874439461883408, "acc_stderr": 0.03304062175449297, "acc_norm": 0.5874439461883408, "acc_norm_stderr": 0.03304062175449297 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.4732824427480916, "acc_stderr": 0.04379024936553894, "acc_norm": 0.4732824427480916, "acc_norm_stderr": 0.04379024936553894 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6115702479338843, "acc_stderr": 0.044492703500683836, "acc_norm": 0.6115702479338843, "acc_norm_stderr": 0.044492703500683836 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5833333333333334, "acc_stderr": 0.04766075165356461, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.04766075165356461 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.50920245398773, "acc_stderr": 0.03927705600787443, "acc_norm": 0.50920245398773, "acc_norm_stderr": 0.03927705600787443 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.41964285714285715, "acc_stderr": 0.04684099321077106, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.04684099321077106 }, "harness|hendrycksTest-management|5": { "acc": 0.6601941747572816, "acc_stderr": 0.04689765937278135, "acc_norm": 0.6601941747572816, "acc_norm_stderr": 0.04689765937278135 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7692307692307693, "acc_stderr": 0.027601921381417593, "acc_norm": 0.7692307692307693, "acc_norm_stderr": 0.027601921381417593 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6883780332056194, "acc_stderr": 0.016562433867284176, "acc_norm": 0.6883780332056194, "acc_norm_stderr": 0.016562433867284176 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.48554913294797686, "acc_stderr": 0.026907849856282542, "acc_norm": 0.48554913294797686, "acc_norm_stderr": 0.026907849856282542 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.25921787709497207, "acc_stderr": 0.014655780837497731, "acc_norm": 0.25921787709497207, "acc_norm_stderr": 0.014655780837497731 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5032679738562091, "acc_stderr": 0.028629305194003543, "acc_norm": 0.5032679738562091, "acc_norm_stderr": 0.028629305194003543 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5819935691318328, "acc_stderr": 0.028013651891995072, "acc_norm": 0.5819935691318328, "acc_norm_stderr": 0.028013651891995072 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5432098765432098, "acc_stderr": 0.027716661650194038, "acc_norm": 0.5432098765432098, "acc_norm_stderr": 0.027716661650194038 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3723404255319149, "acc_stderr": 0.028838921471251455, "acc_norm": 0.3723404255319149, "acc_norm_stderr": 0.028838921471251455 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.34419817470664926, "acc_stderr": 0.01213443374100257, "acc_norm": 0.34419817470664926, "acc_norm_stderr": 0.01213443374100257 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.38235294117647056, "acc_stderr": 0.02952009569768776, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.02952009569768776 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.46078431372549017, "acc_stderr": 0.02016552331390791, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.02016552331390791 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5545454545454546, "acc_stderr": 0.047605488214603246, "acc_norm": 0.5545454545454546, "acc_norm_stderr": 0.047605488214603246 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5387755102040817, "acc_stderr": 0.03191282052669278, "acc_norm": 0.5387755102040817, "acc_norm_stderr": 0.03191282052669278 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6069651741293532, "acc_stderr": 0.0345368246603156, "acc_norm": 0.6069651741293532, "acc_norm_stderr": 0.0345368246603156 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-virology|5": { "acc": 0.41566265060240964, "acc_stderr": 0.03836722176598053, "acc_norm": 0.41566265060240964, "acc_norm_stderr": 0.03836722176598053 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6549707602339181, "acc_stderr": 0.036459813773888065, "acc_norm": 0.6549707602339181, "acc_norm_stderr": 0.036459813773888065 }, "harness|truthfulqa:mc|0": { "mc1": 0.25458996328029376, "mc1_stderr": 0.01525011707915648, "mc2": 0.4122590137037498, "mc2_stderr": 0.014277193708018924 }, "harness|winogrande|5": { "acc": 0.7158642462509865, "acc_stderr": 0.012675392786772727 }, "harness|gsm8k|5": { "acc": 0.11296436694465505, "acc_stderr": 0.008719339028833067 } } ``` ## 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]
mtc/full_cleaned_xsum_faith
--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: document dtype: string - name: claim dtype: string - name: bbcid dtype: string - name: model_name dtype: string - name: label dtype: string - name: split dtype: string - name: annotations sequence: string splits: - name: test num_bytes: 3097533.036 num_examples: 1247 - name: train num_bytes: 2639459.862857143 num_examples: 1048 - name: validation num_bytes: 451054.0 num_examples: 200 download_size: 2120822 dataset_size: 6188046.898857143 --- # Dataset Card for "full_cleaned_xsum_faith" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
owanr/o1o2o3_xl_r2_iterater_with_human_pref_practice
--- dataset_info: features: - name: src dtype: string - name: tgt dtype: string splits: - name: train num_bytes: 13302090 num_examples: 35644 - name: val num_bytes: 649176 num_examples: 1692 - name: test num_bytes: 666158 num_examples: 1707 download_size: 2420178 dataset_size: 14617424 --- # Dataset Card for "o1o2o3_xl_r2_iterater_with_human_pref_practice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EmileEsmaili/sheet_music_ede2110
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 2229356112.491 num_examples: 9219 download_size: 1211789844 dataset_size: 2229356112.491 --- # Dataset Card for "sheet_music_ede2110" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TobiasRobotics/brisbane-event-vpr
--- license: cc-by-nc-sa-4.0 tags: - computer vision - robotics - event cameras pretty_name: Brisbane Event VPR arxiv: 2006.02826 --- This dataset accompanies the following publication, please cite this publication if you use this dataset: Fischer, T. and Milford, M., 2020. Event-Based Visual Place Recognition With Ensembles of Temporal Windows. IEEE Robotics and Automation Letters, 5(4), pp.6924-6931. ```bibtex @article{fischer2020event, title={Event-Based Visual Place Recognition With Ensembles of Temporal Windows}, author={Fischer, Tobias and Milford, Michael}, journal={IEEE Robotics and Automation Letters}, volume={5}, number={4}, pages={6924--6931}, year={2020} } ``` The dataset contains five sequences of recordings. For each recording, a denoised `parquet` file is made available. The source files for these `parquet` files can be found on [Zenodo](https://zenodo.org/records/4302805). We also provide associated GPS information (`*.nmea`) files recorded using the consumer camera. Please see the [associated code repository](https://github.com/Tobias-Fischer/sparse-event-vpr) for more information.
CyberHarem/cygnet_azurlane
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of cygnet/シグニット/小天鹅 (Azur Lane) This is the dataset of cygnet/シグニット/小天鹅 (Azur Lane), containing 283 images and their tags. The core tags of this character are `long_hair, breasts, large_breasts, white_hair, hair_bun, red_eyes, double_bun, braid, ribbon, bangs, hat, bow, ahoge, purple_eyes, hair_ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 283 | 396.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cygnet_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 283 | 221.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cygnet_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 719 | 492.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cygnet_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 283 | 348.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cygnet_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 719 | 702.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cygnet_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/cygnet_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 | 7 | ![](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, blush, cleavage, looking_at_viewer, plaid_bikini, solo, straw_hat, hair_ornament, flower, navel, purple_bikini, innertube, sitting, official_alternate_costume, outdoors, smile | | 1 | 5 | ![](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, black_thighhighs, blush, garter_straps, long_sleeves, shirt, solo, bursting_breasts, looking_at_viewer, retrofit_(azur_lane), button_gap, cleavage, open_mouth, purple_skirt, white_background, choker, plaid, simple_background | | 2 | 8 | ![](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, hair_ornament, plaid, solo, choker, garter_straps, looking_at_viewer, purple_skirt, short_sleeves, blush, simple_background, black_thighhighs, purple_necktie, white_background, open_mouth, retrofit_(azur_lane), white_shirt | | 3 | 6 | ![](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, blush, bursting_breasts, cleavage, collared_shirt, solo, white_shirt, braided_bun, button_gap, light_purple_hair, long_sleeves, purple_skirt, retrofit_(azur_lane), taut_shirt, looking_at_viewer, plaid, simple_background, upper_body, very_long_hair, huge_breasts, twitter_username, undersized_clothes, white_background | | 4 | 43 | ![](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, maid_headdress, solo, looking_at_viewer, cleavage, detached_collar, bare_shoulders, blue_dress, white_thighhighs, white_apron, wrist_cuffs, garter_straps, frilled_apron, waist_apron, hair_bow, blue_bow, collarbone, frilled_dress, very_long_hair, alternate_costume, braided_bun, white_collar, holding, strapless | | 5 | 17 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, blush, christmas, garter_straps, looking_at_viewer, red_gloves, santa_costume, solo, white_thighhighs, capelet, skirt, bell, fur_trim, hair_ornament, navel, cleavage, midriff, light_purple_hair, gift_box | | 6 | 48 | ![](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, cheerleader, navel, elbow_gloves, white_gloves, blush, solo, looking_at_viewer, pom_pom_(cheerleading), purple_skirt, midriff, open_mouth, bare_shoulders, white_belt, white_thighhighs, miniskirt, sleeveless_shirt, sweat, yellow_ribbon, pleated_skirt, white_shirt, holding, whistle_around_neck, collared_shirt, black_choker, zettai_ryouiki, crop_top | | 7 | 9 | ![](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, looking_at_viewer, solo, blush, choker, holding, flower, hairclip, pink_kimono, smile, obi, umbrella, wide_sleeves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | cleavage | looking_at_viewer | plaid_bikini | solo | straw_hat | hair_ornament | flower | navel | purple_bikini | innertube | sitting | official_alternate_costume | outdoors | smile | black_thighhighs | garter_straps | long_sleeves | shirt | bursting_breasts | retrofit_(azur_lane) | button_gap | open_mouth | purple_skirt | white_background | choker | plaid | simple_background | short_sleeves | purple_necktie | white_shirt | collared_shirt | braided_bun | light_purple_hair | taut_shirt | upper_body | very_long_hair | huge_breasts | twitter_username | undersized_clothes | maid_headdress | detached_collar | bare_shoulders | blue_dress | white_thighhighs | white_apron | wrist_cuffs | frilled_apron | waist_apron | hair_bow | blue_bow | collarbone | frilled_dress | alternate_costume | white_collar | holding | strapless | christmas | red_gloves | santa_costume | capelet | skirt | bell | fur_trim | midriff | gift_box | cheerleader | elbow_gloves | white_gloves | pom_pom_(cheerleading) | white_belt | miniskirt | sleeveless_shirt | sweat | yellow_ribbon | pleated_skirt | whistle_around_neck | black_choker | zettai_ryouiki | crop_top | hairclip | pink_kimono | obi | umbrella | wide_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------|:--------------------|:---------------|:-------|:------------|:----------------|:---------|:--------|:----------------|:------------|:----------|:-----------------------------|:-----------|:--------|:-------------------|:----------------|:---------------|:--------|:-------------------|:-----------------------|:-------------|:-------------|:---------------|:-------------------|:---------|:--------|:--------------------|:----------------|:-----------------|:--------------|:-----------------|:--------------|:--------------------|:-------------|:-------------|:-----------------|:---------------|:-------------------|:---------------------|:-----------------|:------------------|:-----------------|:-------------|:-------------------|:--------------|:--------------|:----------------|:--------------|:-----------|:-----------|:-------------|:----------------|:--------------------|:---------------|:----------|:------------|:------------|:-------------|:----------------|:----------|:--------|:-------|:-----------|:----------|:-----------|:--------------|:---------------|:---------------|:-------------------------|:-------------|:------------|:-------------------|:--------|:----------------|:----------------|:----------------------|:---------------|:-----------------|:-----------|:-----------|:--------------|:------|:-----------|:---------------| | 0 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | | X | | X | | | | | | | | | X | X | | | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | | X | | | | | | | | | | | | | X | | X | X | X | | X | X | | X | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 43 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 17 | ![](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 | | | | | | | | | | | | | | | | | | | | | 6 | 48 | ![](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 | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | 7 | 9 | ![](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 |
bjoernp/gaps_spa
--- dataset_info: features: - name: sentences dtype: string - name: sentences_sp dtype: string splits: - name: train num_bytes: 59056357510 num_examples: 231500660 download_size: 34172826813 dataset_size: 59056357510 --- # Dataset Card for "gaps_spa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FUBUKIBG/rosepronto1
--- license: openrail ---
open-llm-leaderboard/details_GritLM__GritLM-8x7B
--- pretty_name: Evaluation run of GritLM/GritLM-8x7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [GritLM/GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) 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_GritLM__GritLM-8x7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-10T14:24:41.959625](https://huggingface.co/datasets/open-llm-leaderboard/details_GritLM__GritLM-8x7B/blob/main/results_2024-03-10T14-24-41.959625.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\": 0.7125539724588248,\n\ \ \"acc_stderr\": 0.0303257615066163,\n \"acc_norm\": 0.7161704881482764,\n\ \ \"acc_norm_stderr\": 0.030919401183717173,\n \"mc1\": 0.33659730722154224,\n\ \ \"mc1_stderr\": 0.016542412809494884,\n \"mc2\": 0.4947094775859723,\n\ \ \"mc2_stderr\": 0.014373065476642853\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6450511945392492,\n \"acc_stderr\": 0.013983036904094085,\n\ \ \"acc_norm\": 0.6774744027303754,\n \"acc_norm_stderr\": 0.01365998089427737\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6650069707229636,\n\ \ \"acc_stderr\": 0.004710234188047369,\n \"acc_norm\": 0.865166301533559,\n\ \ \"acc_norm_stderr\": 0.003408478333768264\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\": 0.674074074074074,\n\ \ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.674074074074074,\n\ \ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.031103182383123384,\n\ \ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.031103182383123384\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7962264150943397,\n \"acc_stderr\": 0.024790784501775402,\n\ \ \"acc_norm\": 0.7962264150943397,\n \"acc_norm_stderr\": 0.024790784501775402\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\ \ \"acc_stderr\": 0.03216600808802268,\n \"acc_norm\": 0.8194444444444444,\n\ \ \"acc_norm_stderr\": 0.03216600808802268\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.0349610148119118,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.0349610148119118\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6851063829787234,\n \"acc_stderr\": 0.030363582197238174,\n\ \ \"acc_norm\": 0.6851063829787234,\n \"acc_norm_stderr\": 0.030363582197238174\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6228070175438597,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.6228070175438597,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.03996629574876719,\n\ \ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.03996629574876719\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4947089947089947,\n \"acc_stderr\": 0.02574986828855657,\n \"\ acc_norm\": 0.4947089947089947,\n \"acc_norm_stderr\": 0.02574986828855657\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.6031746031746031,\n\ \ \"acc_stderr\": 0.0437588849272706,\n \"acc_norm\": 0.6031746031746031,\n\ \ \"acc_norm_stderr\": 0.0437588849272706\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8387096774193549,\n\ \ \"acc_stderr\": 0.020923327006423298,\n \"acc_norm\": 0.8387096774193549,\n\ \ \"acc_norm_stderr\": 0.020923327006423298\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6059113300492611,\n \"acc_stderr\": 0.034381579670365446,\n\ \ \"acc_norm\": 0.6059113300492611,\n \"acc_norm_stderr\": 0.034381579670365446\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483016,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483016\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8484848484848485,\n \"acc_stderr\": 0.02554565042660362,\n \"\ acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.02554565042660362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9430051813471503,\n \"acc_stderr\": 0.01673108529360755,\n\ \ \"acc_norm\": 0.9430051813471503,\n \"acc_norm_stderr\": 0.01673108529360755\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7076923076923077,\n \"acc_stderr\": 0.023060438380857737,\n\ \ \"acc_norm\": 0.7076923076923077,\n \"acc_norm_stderr\": 0.023060438380857737\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3851851851851852,\n \"acc_stderr\": 0.029670906124630886,\n \ \ \"acc_norm\": 0.3851851851851852,\n \"acc_norm_stderr\": 0.029670906124630886\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8109243697478992,\n \"acc_stderr\": 0.02543511943810535,\n \ \ \"acc_norm\": 0.8109243697478992,\n \"acc_norm_stderr\": 0.02543511943810535\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4503311258278146,\n \"acc_stderr\": 0.04062290018683775,\n \"\ acc_norm\": 0.4503311258278146,\n \"acc_norm_stderr\": 0.04062290018683775\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8844036697247707,\n \"acc_stderr\": 0.01370874953417264,\n \"\ acc_norm\": 0.8844036697247707,\n \"acc_norm_stderr\": 0.01370874953417264\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6435185185185185,\n \"acc_stderr\": 0.032664783315272714,\n \"\ acc_norm\": 0.6435185185185185,\n \"acc_norm_stderr\": 0.032664783315272714\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8676470588235294,\n \"acc_stderr\": 0.02378429752091885,\n \"\ acc_norm\": 0.8676470588235294,\n \"acc_norm_stderr\": 0.02378429752091885\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8523206751054853,\n \"acc_stderr\": 0.0230943295825957,\n \ \ \"acc_norm\": 0.8523206751054853,\n \"acc_norm_stderr\": 0.0230943295825957\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7802690582959642,\n\ \ \"acc_stderr\": 0.027790177064383595,\n \"acc_norm\": 0.7802690582959642,\n\ \ \"acc_norm_stderr\": 0.027790177064383595\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476074,\n\ \ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476074\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807193,\n \"\ acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807193\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8518518518518519,\n\ \ \"acc_stderr\": 0.03434300243631,\n \"acc_norm\": 0.8518518518518519,\n\ \ \"acc_norm_stderr\": 0.03434300243631\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\": 0.5982142857142857,\n\ \ \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.5982142857142857,\n\ \ \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761012,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761012\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132366,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132366\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8697318007662835,\n\ \ \"acc_stderr\": 0.012036729568216054,\n \"acc_norm\": 0.8697318007662835,\n\ \ \"acc_norm_stderr\": 0.012036729568216054\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7890173410404624,\n \"acc_stderr\": 0.021966309947043114,\n\ \ \"acc_norm\": 0.7890173410404624,\n \"acc_norm_stderr\": 0.021966309947043114\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4782122905027933,\n\ \ \"acc_stderr\": 0.016706617522176132,\n \"acc_norm\": 0.4782122905027933,\n\ \ \"acc_norm_stderr\": 0.016706617522176132\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7810457516339869,\n \"acc_stderr\": 0.02367908986180772,\n\ \ \"acc_norm\": 0.7810457516339869,\n \"acc_norm_stderr\": 0.02367908986180772\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8006430868167203,\n\ \ \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.8006430868167203,\n\ \ \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8302469135802469,\n \"acc_stderr\": 0.020888690414093868,\n\ \ \"acc_norm\": 0.8302469135802469,\n \"acc_norm_stderr\": 0.020888690414093868\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5460992907801419,\n \"acc_stderr\": 0.029700453247291467,\n \ \ \"acc_norm\": 0.5460992907801419,\n \"acc_norm_stderr\": 0.029700453247291467\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5234680573663625,\n\ \ \"acc_stderr\": 0.012756161942523339,\n \"acc_norm\": 0.5234680573663625,\n\ \ \"acc_norm_stderr\": 0.012756161942523339\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7977941176470589,\n \"acc_stderr\": 0.024398192986654924,\n\ \ \"acc_norm\": 0.7977941176470589,\n \"acc_norm_stderr\": 0.024398192986654924\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7663398692810458,\n \"acc_stderr\": 0.017119158496044506,\n \ \ \"acc_norm\": 0.7663398692810458,\n \"acc_norm_stderr\": 0.017119158496044506\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866764,\n\ \ \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866764\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n\ \ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\ \ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n\ \ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.33659730722154224,\n\ \ \"mc1_stderr\": 0.016542412809494884,\n \"mc2\": 0.4947094775859723,\n\ \ \"mc2_stderr\": 0.014373065476642853\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8279400157853196,\n \"acc_stderr\": 0.010607731615247005\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6163760424564063,\n \ \ \"acc_stderr\": 0.013394238584938161\n }\n}\n```" repo_url: https://huggingface.co/GritLM/GritLM-8x7B 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_21T03_21_40.484316 path: - '**/details_harness|arc:challenge|25_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|arc:challenge|25_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-10T14-24-41.959625.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|gsm8k|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|gsm8k|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hellaswag|10_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hellaswag|10_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-21-40.484316.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-10T14-24-41.959625.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-management|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-management|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T14-24-41.959625.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|truthfulqa:mc|0_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|truthfulqa:mc|0_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-10T14-24-41.959625.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_21T03_21_40.484316 path: - '**/details_harness|winogrande|5_2024-02-21T03-21-40.484316.parquet' - split: 2024_03_10T14_24_41.959625 path: - '**/details_harness|winogrande|5_2024-03-10T14-24-41.959625.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-10T14-24-41.959625.parquet' - config_name: results data_files: - split: 2024_02_21T03_21_40.484316 path: - results_2024-02-21T03-21-40.484316.parquet - split: 2024_03_10T14_24_41.959625 path: - results_2024-03-10T14-24-41.959625.parquet - split: latest path: - results_2024-03-10T14-24-41.959625.parquet --- # Dataset Card for Evaluation run of GritLM/GritLM-8x7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [GritLM/GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) 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_GritLM__GritLM-8x7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-10T14:24:41.959625](https://huggingface.co/datasets/open-llm-leaderboard/details_GritLM__GritLM-8x7B/blob/main/results_2024-03-10T14-24-41.959625.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": 0.7125539724588248, "acc_stderr": 0.0303257615066163, "acc_norm": 0.7161704881482764, "acc_norm_stderr": 0.030919401183717173, "mc1": 0.33659730722154224, "mc1_stderr": 0.016542412809494884, "mc2": 0.4947094775859723, "mc2_stderr": 0.014373065476642853 }, "harness|arc:challenge|25": { "acc": 0.6450511945392492, "acc_stderr": 0.013983036904094085, "acc_norm": 0.6774744027303754, "acc_norm_stderr": 0.01365998089427737 }, "harness|hellaswag|10": { "acc": 0.6650069707229636, "acc_stderr": 0.004710234188047369, "acc_norm": 0.865166301533559, "acc_norm_stderr": 0.003408478333768264 }, "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": 0.674074074074074, "acc_stderr": 0.040491220417025055, "acc_norm": 0.674074074074074, "acc_norm_stderr": 0.040491220417025055 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8223684210526315, "acc_stderr": 0.031103182383123384, "acc_norm": 0.8223684210526315, "acc_norm_stderr": 0.031103182383123384 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7962264150943397, "acc_stderr": 0.024790784501775402, "acc_norm": 0.7962264150943397, "acc_norm_stderr": 0.024790784501775402 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8194444444444444, "acc_stderr": 0.03216600808802268, "acc_norm": 0.8194444444444444, "acc_norm_stderr": 0.03216600808802268 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6994219653179191, "acc_stderr": 0.0349610148119118, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.0349610148119118 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6851063829787234, "acc_stderr": 0.030363582197238174, "acc_norm": 0.6851063829787234, "acc_norm_stderr": 0.030363582197238174 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6228070175438597, "acc_stderr": 0.04559522141958216, "acc_norm": 0.6228070175438597, "acc_norm_stderr": 0.04559522141958216 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6413793103448275, "acc_stderr": 0.03996629574876719, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.03996629574876719 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4947089947089947, "acc_stderr": 0.02574986828855657, "acc_norm": 0.4947089947089947, "acc_norm_stderr": 0.02574986828855657 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.6031746031746031, "acc_stderr": 0.0437588849272706, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.0437588849272706 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8387096774193549, "acc_stderr": 0.020923327006423298, "acc_norm": 0.8387096774193549, "acc_norm_stderr": 0.020923327006423298 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6059113300492611, "acc_stderr": 0.034381579670365446, "acc_norm": 0.6059113300492611, "acc_norm_stderr": 0.034381579670365446 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483016, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483016 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8484848484848485, "acc_stderr": 0.02554565042660362, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.02554565042660362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9430051813471503, "acc_stderr": 0.01673108529360755, "acc_norm": 0.9430051813471503, "acc_norm_stderr": 0.01673108529360755 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7076923076923077, "acc_stderr": 0.023060438380857737, "acc_norm": 0.7076923076923077, "acc_norm_stderr": 0.023060438380857737 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3851851851851852, "acc_stderr": 0.029670906124630886, "acc_norm": 0.3851851851851852, "acc_norm_stderr": 0.029670906124630886 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8109243697478992, "acc_stderr": 0.02543511943810535, "acc_norm": 0.8109243697478992, "acc_norm_stderr": 0.02543511943810535 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4503311258278146, "acc_stderr": 0.04062290018683775, "acc_norm": 0.4503311258278146, "acc_norm_stderr": 0.04062290018683775 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8844036697247707, "acc_stderr": 0.01370874953417264, "acc_norm": 0.8844036697247707, "acc_norm_stderr": 0.01370874953417264 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6435185185185185, "acc_stderr": 0.032664783315272714, "acc_norm": 0.6435185185185185, "acc_norm_stderr": 0.032664783315272714 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8676470588235294, "acc_stderr": 0.02378429752091885, "acc_norm": 0.8676470588235294, "acc_norm_stderr": 0.02378429752091885 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8523206751054853, "acc_stderr": 0.0230943295825957, "acc_norm": 0.8523206751054853, "acc_norm_stderr": 0.0230943295825957 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7802690582959642, "acc_stderr": 0.027790177064383595, "acc_norm": 0.7802690582959642, "acc_norm_stderr": 0.027790177064383595 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8244274809160306, "acc_stderr": 0.03336820338476074, "acc_norm": 0.8244274809160306, "acc_norm_stderr": 0.03336820338476074 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8512396694214877, "acc_stderr": 0.03248470083807193, "acc_norm": 0.8512396694214877, "acc_norm_stderr": 0.03248470083807193 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8518518518518519, "acc_stderr": 0.03434300243631, "acc_norm": 0.8518518518518519, "acc_norm_stderr": 0.03434300243631 }, "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": 0.5982142857142857, "acc_stderr": 0.04653333146973647, "acc_norm": 0.5982142857142857, "acc_norm_stderr": 0.04653333146973647 }, "harness|hendrycksTest-management|5": { "acc": 0.8640776699029126, "acc_stderr": 0.03393295729761012, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.03393295729761012 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9273504273504274, "acc_stderr": 0.017004368568132366, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.017004368568132366 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8697318007662835, "acc_stderr": 0.012036729568216054, "acc_norm": 0.8697318007662835, "acc_norm_stderr": 0.012036729568216054 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7890173410404624, "acc_stderr": 0.021966309947043114, "acc_norm": 0.7890173410404624, "acc_norm_stderr": 0.021966309947043114 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4782122905027933, "acc_stderr": 0.016706617522176132, "acc_norm": 0.4782122905027933, "acc_norm_stderr": 0.016706617522176132 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7810457516339869, "acc_stderr": 0.02367908986180772, "acc_norm": 0.7810457516339869, "acc_norm_stderr": 0.02367908986180772 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8006430868167203, "acc_stderr": 0.022691033780549656, "acc_norm": 0.8006430868167203, "acc_norm_stderr": 0.022691033780549656 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8302469135802469, "acc_stderr": 0.020888690414093868, "acc_norm": 0.8302469135802469, "acc_norm_stderr": 0.020888690414093868 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5460992907801419, "acc_stderr": 0.029700453247291467, "acc_norm": 0.5460992907801419, "acc_norm_stderr": 0.029700453247291467 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5234680573663625, "acc_stderr": 0.012756161942523339, "acc_norm": 0.5234680573663625, "acc_norm_stderr": 0.012756161942523339 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7977941176470589, "acc_stderr": 0.024398192986654924, "acc_norm": 0.7977941176470589, "acc_norm_stderr": 0.024398192986654924 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7663398692810458, "acc_stderr": 0.017119158496044506, "acc_norm": 0.7663398692810458, "acc_norm_stderr": 0.017119158496044506 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7877551020408163, "acc_stderr": 0.026176967197866764, "acc_norm": 0.7877551020408163, "acc_norm_stderr": 0.026176967197866764 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8805970149253731, "acc_stderr": 0.02292879327721974, "acc_norm": 0.8805970149253731, "acc_norm_stderr": 0.02292879327721974 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.89, "acc_stderr": 0.03144660377352202, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352202 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8830409356725146, "acc_stderr": 0.024648068961366152, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.024648068961366152 }, "harness|truthfulqa:mc|0": { "mc1": 0.33659730722154224, "mc1_stderr": 0.016542412809494884, "mc2": 0.4947094775859723, "mc2_stderr": 0.014373065476642853 }, "harness|winogrande|5": { "acc": 0.8279400157853196, "acc_stderr": 0.010607731615247005 }, "harness|gsm8k|5": { "acc": 0.6163760424564063, "acc_stderr": 0.013394238584938161 } } ``` ## 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]
ACT8113/DylanKlebold
--- license: openrail ---
polejowska/MIST1-brain-gt-tumors
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects list: - name: category_id dtype: class_label: names: '0': mist1 - name: image_id dtype: string - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: segmentation list: list: float32 - name: iscrowd dtype: bool splits: - name: train num_bytes: 905343541.0 num_examples: 460 - name: valid num_bytes: 75913475.0 num_examples: 40 - name: test num_bytes: 48033661.0 num_examples: 25 download_size: 1029189310 dataset_size: 1029290677.0 --- # Dataset Card for "MIST1-brain-gt-tumors" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rahulvyasm/medical_insurance_data
--- license: mit --- # Dataset Card for Medical Insurance Cost Prediction The medical insurance dataset encompasses various factors influencing medical expenses, such as age, sex, BMI, smoking status, number of children, and region. This dataset serves as a foundation for training machine learning models capable of forecasting medical expenses for new policyholders. Its purpose is to shed light on the pivotal elements contributing to increased insurance costs, aiding the company in making more informed decisions concerning pricing and risk assessment. ## Dataset Description The dataset contains **2.7K rows** and **7 columns** **Columns include** 1. Age 2. Sex 3. BMI (Body Mass Index) 4. Children 5. Smoker 6. Region 7. Charges #### Table of Contents - [Introduction](#introduction) - [Problem Statement](#problem-statement) - [Features](#features) - [Technologies Used](#technologies-used) - [Usage](#usage) - [Installation](#installation) - [Data Preparation](#data-preparation) - [Model Training](#model-training) - [Model Evaluation](#model-evaluation) - [Model Serialization](#model-serialization) - [Contributors](#contributors) - [License](#license) #### Introduction Healthcare costs are a significant concern for individuals and families worldwide. Predicting medical insurance costs accurately can help insurance companies determine premiums and assist individuals in planning their healthcare expenses. This project focuses on building machine learning models to predict insurance costs based on demographic and health-related attributes. #### Problem Statement 1. What are the most important factors that affect medical expenses? 2. How well can machine learning models predict medical expenses? 3. How can machine learning models be used to improve the efficiency and profitability of health insurance companies? #### Features - **Data Exploration**: Explore the dataset to understand its structure, identify missing values, and analyze the distribution of features. - **Data Preprocessing**: Prepare the data by handling categorical variables, renaming columns, and scaling numerical features. - **Model Training**: Utilize linear regression and ridge regression models to train predictive models on the prepared dataset. - **Pipeline Construction**: Construct a data preprocessing pipeline to streamline the process of transforming input data for model training. - **Model Evaluation**: Evaluate model performance using metrics such as R-squared score and mean squared error to assess predictive accuracy. - **Model Serialization**: Save trained models and pipelines to disk using the pickle library for future use. #### Technologies Used - **Python**: Programming language used for data manipulation, analysis, and model implementation. - **Libraries**: NumPy, Pandas, Seaborn, Matplotlib, and Scikit-learn for data handling, visualization, and machine learning tasks. - **Machine Learning Models**: Linear Regression, Ridge Regression - **Pickle**: Python library used for serializing trained models and pipelines to disk. ### Dataset Sources From multiple online and offline datasets ## Problem Statement 1. What are the primary factors influencing medical expenses? 2. How accurate are machine learning models in predicting medical expenses? 3. In what ways can machine learning models enhance the efficiency and profitability of health insurance companies?
open-llm-leaderboard/details_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch
--- pretty_name: Evaluation run of TFLai/Athena-Platypus2-13B-QLora-0.80-epoch dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TFLai/Athena-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Athena-Platypus2-13B-QLora-0.80-epoch)\ \ 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_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-21T23:00:07.727248](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-21T23-00-07.727248.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\": 0.10455117449664429,\n\ \ \"em_stderr\": 0.0031334624512179676,\n \"f1\": 0.22509018456375873,\n\ \ \"f1_stderr\": 0.0034177949703821024,\n \"acc\": 0.3634414270694895,\n\ \ \"acc_stderr\": 0.006645721423171415\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.10455117449664429,\n \"em_stderr\": 0.0031334624512179676,\n\ \ \"f1\": 0.22509018456375873,\n \"f1_stderr\": 0.0034177949703821024\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \ \ \"acc_stderr\": 0.0007581501137225331\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620297\n\ \ }\n}\n```" repo_url: https://huggingface.co/TFLai/Athena-Platypus2-13B-QLora-0.80-epoch leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|arc:challenge|25_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-30T12:24:23.685858.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_21T23_00_07.727248 path: - '**/details_harness|drop|3_2023-10-21T23-00-07.727248.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-21T23-00-07.727248.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_21T23_00_07.727248 path: - '**/details_harness|gsm8k|5_2023-10-21T23-00-07.727248.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-21T23-00-07.727248.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hellaswag|10_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-30T12:24:23.685858.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-management|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T12:24:23.685858.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_30T12_24_23.685858 path: - '**/details_harness|truthfulqa:mc|0_2023-08-30T12:24:23.685858.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-30T12:24:23.685858.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_21T23_00_07.727248 path: - '**/details_harness|winogrande|5_2023-10-21T23-00-07.727248.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-21T23-00-07.727248.parquet' - config_name: results data_files: - split: 2023_08_30T12_24_23.685858 path: - results_2023-08-30T12:24:23.685858.parquet - split: 2023_10_21T23_00_07.727248 path: - results_2023-10-21T23-00-07.727248.parquet - split: latest path: - results_2023-10-21T23-00-07.727248.parquet --- # Dataset Card for Evaluation run of TFLai/Athena-Platypus2-13B-QLora-0.80-epoch ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TFLai/Athena-Platypus2-13B-QLora-0.80-epoch - **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 [TFLai/Athena-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Athena-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-21T23:00:07.727248](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-21T23-00-07.727248.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": 0.10455117449664429, "em_stderr": 0.0031334624512179676, "f1": 0.22509018456375873, "f1_stderr": 0.0034177949703821024, "acc": 0.3634414270694895, "acc_stderr": 0.006645721423171415 }, "harness|drop|3": { "em": 0.10455117449664429, "em_stderr": 0.0031334624512179676, "f1": 0.22509018456375873, "f1_stderr": 0.0034177949703821024 }, "harness|gsm8k|5": { "acc": 0.000758150113722517, "acc_stderr": 0.0007581501137225331 }, "harness|winogrande|5": { "acc": 0.7261247040252565, "acc_stderr": 0.012533292732620297 } } ``` ### 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]
MatsuoDochiai/Sumpomni
--- license: openrail ---
VaibhavGp69/Aarogya_MedText
--- dataset_info: features: - name: Aarogya_prompt dtype: string - name: Prompt dtype: string - name: Completion dtype: string splits: - name: train num_bytes: 1305200 num_examples: 1412 download_size: 658986 dataset_size: 1305200 configs: - config_name: default data_files: - split: train path: data/train-* ---
liuyanchen1015/MULTI_VALUE_qqp_analytic_whose_relativizer
--- 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: 3705 num_examples: 17 - name: test num_bytes: 39408 num_examples: 188 - name: train num_bytes: 39665 num_examples: 183 download_size: 57861 dataset_size: 82778 --- # Dataset Card for "MULTI_VALUE_qqp_analytic_whose_relativizer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nan-Do/instructional_code-search-net-php
--- dataset_info: features: - name: INSTRUCTION dtype: string - name: RESPONSE dtype: string - name: SOURCE dtype: string splits: - name: train num_bytes: 448756286 num_examples: 536632 download_size: 158708948 dataset_size: 448756286 license: apache-2.0 task_categories: - conversational - text-generation - text2text-generation language: - en tags: - PHP - Code Generation - Instruction Response pretty_name: Instructional PHP Dataset --- # Dataset Card for "instructional_code-search-net-php" ## Dataset Description - **Homepage:** None - **Repository:** https://huggingface.co/datasets/Nan-Do/instructional_code-search-net-php - **Paper:** None - **Leaderboard:** None - **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do) ### Dataset Summary This is an instructional dataset for PHP. The dataset contains two different kind of tasks: - Given a piece of code generate a description of what it does. - Given a description generate a piece of code that fulfils the description. ### Languages The dataset is in English. ### Data Splits There are no splits. ## Dataset Creation May of 2023 ### Curation Rationale This dataset was created to improve the coding capabilities of LLMs. ### Source Data The summarized version of the code-search-net dataset can be found at https://huggingface.co/datasets/Nan-Do/code-search-net-php ### Annotations The dataset includes an instruction and response columns. #### Annotation process The annotation procedure was done using templates and NLP techniques to generate human-like instructions and responses. A sample notebook of the process can be found at https://github.com/Nan-Do/OpenAssistantInstructionResponsePython The annontations have been cleaned to make sure there are no repetitions and/or meaningless summaries. ### Licensing Information Apache 2.0
huggingface-ml-4-games-course/unity-demos
--- license: apache-2.0 --- # Unity Demos 🎮 This dataset contains the Unity demos for the **[ML for Games course](https://huggingface.co/learn/ml-games-course/unit0/introduction)** The course's link 👉 https://huggingface.co/learn/ml-games-course/unit0/introduction <img src="https://huggingface.co/datasets/huggingface-ml-4-games-course/course-images/resolve/main/en/unit0/thumbnail.jpg" alt="ML for Games course"/>
open-llm-leaderboard/details_Felladrin__Minueza-32Mx2-Chat
--- pretty_name: Evaluation run of Felladrin/Minueza-32Mx2-Chat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Felladrin/Minueza-32Mx2-Chat](https://huggingface.co/Felladrin/Minueza-32Mx2-Chat)\ \ 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_Felladrin__Minueza-32Mx2-Chat\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-09T19:34:20.089689](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__Minueza-32Mx2-Chat/blob/main/results_2024-03-09T19-34-20.089689.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\": 0.25904930626678757,\n\ \ \"acc_stderr\": 0.03081108979139557,\n \"acc_norm\": 0.25975067177423705,\n\ \ \"acc_norm_stderr\": 0.03163420298964525,\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557994,\n \"mc2\": 0.4455926367351534,\n\ \ \"mc2_stderr\": 0.015305936450342793\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.16040955631399317,\n \"acc_stderr\": 0.010724336059110964,\n\ \ \"acc_norm\": 0.20136518771331058,\n \"acc_norm_stderr\": 0.01171892747744427\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2650866361282613,\n\ \ \"acc_stderr\": 0.004404772735765963,\n \"acc_norm\": 0.2635929097789285,\n\ \ \"acc_norm_stderr\": 0.004396806562351326\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.24444444444444444,\n\ \ \"acc_stderr\": 0.037125378336148665,\n \"acc_norm\": 0.24444444444444444,\n\ \ \"acc_norm_stderr\": 0.037125378336148665\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.03782728980865469,\n\ \ \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.03782728980865469\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.21,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.02544786382510861,\n\ \ \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.02544786382510861\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.24305555555555555,\n\ \ \"acc_stderr\": 0.03586879280080343,\n \"acc_norm\": 0.24305555555555555,\n\ \ \"acc_norm_stderr\": 0.03586879280080343\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.36,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n\ \ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\ \ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.18,\n\ \ \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.20425531914893616,\n \"acc_stderr\": 0.026355158413349424,\n\ \ \"acc_norm\": 0.20425531914893616,\n \"acc_norm_stderr\": 0.026355158413349424\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n\ \ \"acc_stderr\": 0.03892431106518754,\n \"acc_norm\": 0.21929824561403508,\n\ \ \"acc_norm_stderr\": 0.03892431106518754\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.036951833116502325,\n\ \ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.036951833116502325\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708617,\n \"\ acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708617\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15873015873015872,\n\ \ \"acc_stderr\": 0.03268454013011743,\n \"acc_norm\": 0.15873015873015872,\n\ \ \"acc_norm_stderr\": 0.03268454013011743\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3161290322580645,\n\ \ \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.3161290322580645,\n\ \ \"acc_norm_stderr\": 0.02645087448904277\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3054187192118227,\n \"acc_stderr\": 0.03240661565868408,\n\ \ \"acc_norm\": 0.3054187192118227,\n \"acc_norm_stderr\": 0.03240661565868408\n\ \ },\n \"harness|hendrycksTest-high_school_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-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\"\ : 0.2676767676767677,\n \"acc_stderr\": 0.03154449888270285,\n \"\ acc_norm\": 0.2676767676767677,\n \"acc_norm_stderr\": 0.03154449888270285\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.32124352331606215,\n \"acc_stderr\": 0.033699508685490674,\n\ \ \"acc_norm\": 0.32124352331606215,\n \"acc_norm_stderr\": 0.033699508685490674\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.36153846153846153,\n \"acc_stderr\": 0.024359581465396983,\n\ \ \"acc_norm\": 0.36153846153846153,\n \"acc_norm_stderr\": 0.024359581465396983\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073828,\n \ \ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073828\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.031041941304059288,\n\ \ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.031041941304059288\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.24220183486238533,\n \"acc_stderr\": 0.01836817630659862,\n \"\ acc_norm\": 0.24220183486238533,\n \"acc_norm_stderr\": 0.01836817630659862\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.2647058823529412,\n \"acc_stderr\": 0.030964517926923403,\n \"\ acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.030964517926923403\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.23628691983122363,\n \"acc_stderr\": 0.02765215314415927,\n \ \ \"acc_norm\": 0.23628691983122363,\n \"acc_norm_stderr\": 0.02765215314415927\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.20179372197309417,\n\ \ \"acc_stderr\": 0.026936111912802273,\n \"acc_norm\": 0.20179372197309417,\n\ \ \"acc_norm_stderr\": 0.026936111912802273\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2900763358778626,\n \"acc_stderr\": 0.03980066246467765,\n\ \ \"acc_norm\": 0.2900763358778626,\n \"acc_norm_stderr\": 0.03980066246467765\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.371900826446281,\n \"acc_stderr\": 0.044120158066245044,\n \"\ acc_norm\": 0.371900826446281,\n \"acc_norm_stderr\": 0.044120158066245044\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.16964285714285715,\n\ \ \"acc_stderr\": 0.035623678500953895,\n \"acc_norm\": 0.16964285714285715,\n\ \ \"acc_norm_stderr\": 0.035623678500953895\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.24271844660194175,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.24271844660194175,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.19658119658119658,\n\ \ \"acc_stderr\": 0.02603538609895129,\n \"acc_norm\": 0.19658119658119658,\n\ \ \"acc_norm_stderr\": 0.02603538609895129\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.26947637292464877,\n\ \ \"acc_stderr\": 0.01586624307321506,\n \"acc_norm\": 0.26947637292464877,\n\ \ \"acc_norm_stderr\": 0.01586624307321506\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.21676300578034682,\n \"acc_stderr\": 0.022183477668412856,\n\ \ \"acc_norm\": 0.21676300578034682,\n \"acc_norm_stderr\": 0.022183477668412856\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767864,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767864\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.23202614379084968,\n \"acc_stderr\": 0.024170840879341026,\n\ \ \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.024170840879341026\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2797427652733119,\n\ \ \"acc_stderr\": 0.025494259350694888,\n \"acc_norm\": 0.2797427652733119,\n\ \ \"acc_norm_stderr\": 0.025494259350694888\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.25308641975308643,\n \"acc_stderr\": 0.024191808600713,\n\ \ \"acc_norm\": 0.25308641975308643,\n \"acc_norm_stderr\": 0.024191808600713\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2765957446808511,\n \"acc_stderr\": 0.026684564340460997,\n \ \ \"acc_norm\": 0.2765957446808511,\n \"acc_norm_stderr\": 0.026684564340460997\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24837027379400262,\n\ \ \"acc_stderr\": 0.011035212598034498,\n \"acc_norm\": 0.24837027379400262,\n\ \ \"acc_norm_stderr\": 0.011035212598034498\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.44485294117647056,\n \"acc_stderr\": 0.030187532060329376,\n\ \ \"acc_norm\": 0.44485294117647056,\n \"acc_norm_stderr\": 0.030187532060329376\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2173202614379085,\n \"acc_stderr\": 0.01668482092914859,\n \ \ \"acc_norm\": 0.2173202614379085,\n \"acc_norm_stderr\": 0.01668482092914859\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.23636363636363636,\n\ \ \"acc_stderr\": 0.04069306319721376,\n \"acc_norm\": 0.23636363636363636,\n\ \ \"acc_norm_stderr\": 0.04069306319721376\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.23265306122448978,\n \"acc_stderr\": 0.027049257915896175,\n\ \ \"acc_norm\": 0.23265306122448978,\n \"acc_norm_stderr\": 0.027049257915896175\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\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.18674698795180722,\n\ \ \"acc_stderr\": 0.03033874914450061,\n \"acc_norm\": 0.18674698795180722,\n\ \ \"acc_norm_stderr\": 0.03033874914450061\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.03126781714663178,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.03126781714663178\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557994,\n \"mc2\": 0.4455926367351534,\n\ \ \"mc2_stderr\": 0.015305936450342793\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.516179952644041,\n \"acc_stderr\": 0.014045126130978608\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/Felladrin/Minueza-32Mx2-Chat leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|arc:challenge|25_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-09T19-34-20.089689.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|gsm8k|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hellaswag|10_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-34-20.089689.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-management|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-34-20.089689.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|truthfulqa:mc|0_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-09T19-34-20.089689.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_09T19_34_20.089689 path: - '**/details_harness|winogrande|5_2024-03-09T19-34-20.089689.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-09T19-34-20.089689.parquet' - config_name: results data_files: - split: 2024_03_09T19_34_20.089689 path: - results_2024-03-09T19-34-20.089689.parquet - split: latest path: - results_2024-03-09T19-34-20.089689.parquet --- # Dataset Card for Evaluation run of Felladrin/Minueza-32Mx2-Chat <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Felladrin/Minueza-32Mx2-Chat](https://huggingface.co/Felladrin/Minueza-32Mx2-Chat) 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_Felladrin__Minueza-32Mx2-Chat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-09T19:34:20.089689](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__Minueza-32Mx2-Chat/blob/main/results_2024-03-09T19-34-20.089689.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": 0.25904930626678757, "acc_stderr": 0.03081108979139557, "acc_norm": 0.25975067177423705, "acc_norm_stderr": 0.03163420298964525, "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557994, "mc2": 0.4455926367351534, "mc2_stderr": 0.015305936450342793 }, "harness|arc:challenge|25": { "acc": 0.16040955631399317, "acc_stderr": 0.010724336059110964, "acc_norm": 0.20136518771331058, "acc_norm_stderr": 0.01171892747744427 }, "harness|hellaswag|10": { "acc": 0.2650866361282613, "acc_stderr": 0.004404772735765963, "acc_norm": 0.2635929097789285, "acc_norm_stderr": 0.004396806562351326 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.24444444444444444, "acc_stderr": 0.037125378336148665, "acc_norm": 0.24444444444444444, "acc_norm_stderr": 0.037125378336148665 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.3157894736842105, "acc_stderr": 0.03782728980865469, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.03782728980865469 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2188679245283019, "acc_stderr": 0.02544786382510861, "acc_norm": 0.2188679245283019, "acc_norm_stderr": 0.02544786382510861 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.24305555555555555, "acc_stderr": 0.03586879280080343, "acc_norm": 0.24305555555555555, "acc_norm_stderr": 0.03586879280080343 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.04784060704105654, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.04784060704105654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.20425531914893616, "acc_stderr": 0.026355158413349424, "acc_norm": 0.20425531914893616, "acc_norm_stderr": 0.026355158413349424 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.21929824561403508, "acc_stderr": 0.03892431106518754, "acc_norm": 0.21929824561403508, "acc_norm_stderr": 0.03892431106518754 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2689655172413793, "acc_stderr": 0.036951833116502325, "acc_norm": 0.2689655172413793, "acc_norm_stderr": 0.036951833116502325 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.26455026455026454, "acc_stderr": 0.022717467897708617, "acc_norm": 0.26455026455026454, "acc_norm_stderr": 0.022717467897708617 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.15873015873015872, "acc_stderr": 0.03268454013011743, "acc_norm": 0.15873015873015872, "acc_norm_stderr": 0.03268454013011743 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3161290322580645, "acc_stderr": 0.02645087448904277, "acc_norm": 0.3161290322580645, "acc_norm_stderr": 0.02645087448904277 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3054187192118227, "acc_stderr": 0.03240661565868408, "acc_norm": 0.3054187192118227, "acc_norm_stderr": 0.03240661565868408 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "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": 0.2676767676767677, "acc_stderr": 0.03154449888270285, "acc_norm": 0.2676767676767677, "acc_norm_stderr": 0.03154449888270285 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.32124352331606215, "acc_stderr": 0.033699508685490674, "acc_norm": 0.32124352331606215, "acc_norm_stderr": 0.033699508685490674 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.36153846153846153, "acc_stderr": 0.024359581465396983, "acc_norm": 0.36153846153846153, "acc_norm_stderr": 0.024359581465396983 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26666666666666666, "acc_stderr": 0.026962424325073828, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.026962424325073828 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.35294117647058826, "acc_stderr": 0.031041941304059288, "acc_norm": 0.35294117647058826, "acc_norm_stderr": 0.031041941304059288 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.24220183486238533, "acc_stderr": 0.01836817630659862, "acc_norm": 0.24220183486238533, "acc_norm_stderr": 0.01836817630659862 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4583333333333333, "acc_stderr": 0.03398110890294636, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2647058823529412, "acc_stderr": 0.030964517926923403, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.030964517926923403 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.23628691983122363, "acc_stderr": 0.02765215314415927, "acc_norm": 0.23628691983122363, "acc_norm_stderr": 0.02765215314415927 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.20179372197309417, "acc_stderr": 0.026936111912802273, "acc_norm": 0.20179372197309417, "acc_norm_stderr": 0.026936111912802273 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2900763358778626, "acc_stderr": 0.03980066246467765, "acc_norm": 0.2900763358778626, "acc_norm_stderr": 0.03980066246467765 }, "harness|hendrycksTest-international_law|5": { "acc": 0.371900826446281, "acc_stderr": 0.044120158066245044, "acc_norm": 0.371900826446281, "acc_norm_stderr": 0.044120158066245044 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.21296296296296297, "acc_stderr": 0.0395783547198098, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.16964285714285715, "acc_stderr": 0.035623678500953895, "acc_norm": 0.16964285714285715, "acc_norm_stderr": 0.035623678500953895 }, "harness|hendrycksTest-management|5": { "acc": 0.24271844660194175, "acc_stderr": 0.04245022486384495, "acc_norm": 0.24271844660194175, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.19658119658119658, "acc_stderr": 0.02603538609895129, "acc_norm": 0.19658119658119658, "acc_norm_stderr": 0.02603538609895129 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.26947637292464877, "acc_stderr": 0.01586624307321506, "acc_norm": 0.26947637292464877, "acc_norm_stderr": 0.01586624307321506 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.21676300578034682, "acc_stderr": 0.022183477668412856, "acc_norm": 0.21676300578034682, "acc_norm_stderr": 0.022183477668412856 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2435754189944134, "acc_stderr": 0.014355911964767864, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.014355911964767864 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.23202614379084968, "acc_stderr": 0.024170840879341026, "acc_norm": 0.23202614379084968, "acc_norm_stderr": 0.024170840879341026 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2797427652733119, "acc_stderr": 0.025494259350694888, "acc_norm": 0.2797427652733119, "acc_norm_stderr": 0.025494259350694888 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.25308641975308643, "acc_stderr": 0.024191808600713, "acc_norm": 0.25308641975308643, "acc_norm_stderr": 0.024191808600713 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2765957446808511, "acc_stderr": 0.026684564340460997, "acc_norm": 0.2765957446808511, "acc_norm_stderr": 0.026684564340460997 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.24837027379400262, "acc_stderr": 0.011035212598034498, "acc_norm": 0.24837027379400262, "acc_norm_stderr": 0.011035212598034498 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.44485294117647056, "acc_stderr": 0.030187532060329376, "acc_norm": 0.44485294117647056, "acc_norm_stderr": 0.030187532060329376 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2173202614379085, "acc_stderr": 0.01668482092914859, "acc_norm": 0.2173202614379085, "acc_norm_stderr": 0.01668482092914859 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.23636363636363636, "acc_stderr": 0.04069306319721376, "acc_norm": 0.23636363636363636, "acc_norm_stderr": 0.04069306319721376 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.23265306122448978, "acc_stderr": 0.027049257915896175, "acc_norm": 0.23265306122448978, "acc_norm_stderr": 0.027049257915896175 }, "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": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-virology|5": { "acc": 0.18674698795180722, "acc_stderr": 0.03033874914450061, "acc_norm": 0.18674698795180722, "acc_norm_stderr": 0.03033874914450061 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03126781714663178, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03126781714663178 }, "harness|truthfulqa:mc|0": { "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557994, "mc2": 0.4455926367351534, "mc2_stderr": 0.015305936450342793 }, "harness|winogrande|5": { "acc": 0.516179952644041, "acc_stderr": 0.014045126130978608 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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]
lucyd/deepgen
--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 27437 num_examples: 267 download_size: 12561 dataset_size: 27437 configs: - config_name: default data_files: - split: train path: data/train-* ---
jondurbin/truthy-dpo-v0.1
--- license: cc-by-4.0 --- ## Truthy DPO This is a dataset designed to enhance the overall truthfulness of LLMs, without sacrificing immersion when roleplaying as a human. For example, in normal AI assistant model, the model should not try to describe what the warmth of the sun feels like, but if the system prompt indicates it's a human, it should. Mostly targets corporeal, spacial, temporal awareness, and common misconceptions. ### Contribute If you're interested in new functionality/datasets, take a look at [bagel repo](https://github.com/jondurbin/bagel) and [airoboros](https://github.com/jondurbin/airoboros) and either make a PR or open an issue with details. To help me with the fine-tuning costs, dataset generation, etc., please use one of the following: - https://bmc.link/jondurbin - ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11 - BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
stockeh/dog-pose-cv
--- license: apache-2.0 task_categories: - image-classification language: - en size_categories: - 10K<n<100K pretty_name: DogPoseCV --- # Dataset Card for DogPoseCV This dataset contains 20,578 images of dogs in various poses, labeled as `standing`, `sitting`, `lying down`, or `undefined`. It is intended for computer vision tasks to identify a dog's behavior from images. ## Dataset Details - **Curated by:** Jason Stock and Tom Cavey, Computer Science, Colorado State University - **Paper:** [arxiv.org/abs/2101.02380](https://arxiv.org/abs/2101.02380) ([BibTeX](#citation)) - **Repository:** [github.com/stockeh/canine-embedded-ml](https://github.com/stockeh/canine-embedded-ml) The dataset is intended to be used to train computer vision models to identify a dog's pose/behavior (standing, sitting, lying down) from images. This can enable applications to automatically detect and respond to a dog's actions. The variety of dog breeds enables robust generalization for real-time inference of dog actions. ### Dataset Structure The dataset contains 20,578 RGB images of 120 dog breeds. Images are labeled as one of four classes: - standing (4143 images) - sitting (3038 images) - lying down (7090 images) - undefined (6307 images) Images have varying resolutions, with 50% between 361x333 and 500x453 pixels. #### Data Collection and Processing This dataset is an adaption of from the [Stanford Dog Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/), relabeling dog breeds to their associated position. We manually labeled each image as `standing`, `sitting`, `lying down`, or `undefined` if the pose was indistinguishable, e.g., between two positions. ## Bias, Risks, and Limitations The dataset has a class imbalance, with nearly 2x as many "lying down" images compared to "sitting". Indistinguishable poses were labeled as "undefined", with most being close-up portraits. This may limit the ability to handle such images. **Recommendations**: When using this dataset, be aware of the class imbalance and consider oversampling or augmentation techniques.. ## Citation ``` @article{stock2021s, title={Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine Learning}, author={Stock, Jason and Cavey, Tom}, journal={arXiv preprint arXiv:2101.02380}, year={2021} } ```
mankness/ecommerce-faq
--- pretty_name: ecommerce-faq ---
jordanfan/processed_us_congress_117_bills_v3
--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: index dtype: int64 - name: id dtype: string - name: policy_areas dtype: string - name: cur_summary dtype: string - name: cur_text dtype: string - name: title dtype: string - name: titles_official dtype: string - name: titles_short dtype: string - name: sponsor_name dtype: string - name: sponsor_party dtype: string - name: sponsor_state dtype: string - name: cleaned_summary dtype: string - name: extracted_text dtype: string - name: extracted_text_375 dtype: string - name: extracted_text_750 dtype: string - name: extracted_text_1000 dtype: string - name: bertsum_extracted_250 dtype: string - name: bertsum_extracted_375 dtype: string - name: bertsum_extracted_375_1000 dtype: string - name: bertsum_extracted_250_1000 dtype: string - name: bertsum_extracted_375_750 dtype: string - name: bertsum_extracted_250_750 dtype: string - name: bertsum_extracted_375_500 dtype: string - name: bertsum_extracted_250_500 dtype: string - name: bertsum_extracted_375_375 dtype: string - name: bertsum_extracted_250_375 dtype: string splits: - name: train num_bytes: 614026113 num_examples: 11277 - name: val num_bytes: 179492083 num_examples: 3388 - name: test num_bytes: 28166503 num_examples: 377 download_size: 355877521 dataset_size: 821684699 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---
autoevaluate/autoeval-eval-futin__feed-top_vi-b5257d-2174969941
--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: bigscience/bloom-3b metrics: [] dataset_name: futin/feed dataset_config: top_vi dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: bigscience/bloom-3b * Dataset: futin/feed * Config: top_vi * 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.
deepalpv/lisa
--- license: osl-3.0 ---
minoruskore/isbl
--- dataset_info: features: - name: image dtype: image - name: tags dtype: string splits: - name: train num_bytes: 406713495.0 num_examples: 32 download_size: 406657072 dataset_size: 406713495.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
DopeorNope/new_instruct_no_ssl
--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 130301895 num_examples: 121332 download_size: 78246124 dataset_size: 130301895 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/myrrh_arknights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of myrrh/ミルラ/末药 (Arknights) This is the dataset of myrrh/ミルラ/末药 (Arknights), containing 87 images and their tags. The core tags of this character are `animal_ears, green_eyes, glasses, red_hair, fox_ears, short_hair, hair_ornament, ahoge, hair_over_one_eye, tail, fox_tail, fox_girl`, 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 | 87 | 128.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrrh_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 87 | 111.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrrh_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 203 | 212.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrrh_arknights/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/myrrh_arknights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 33 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, brown_gloves, shirt, holding, cape, long_sleeves, looking_at_viewer, vial, black_skirt, bandaged_leg, black_choker, id_card, simple_background, thigh_strap, bag, test_tube, white_background, full_body, bow, smile | | 1 | 12 | ![](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, ears_through_headwear, solo, bare_shoulders, black_headwear, holding, short_sleeves, hat, looking_at_viewer, official_alternate_costume, yellow_shirt, backpack, black_scarf, off_shoulder, standing, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | brown_gloves | shirt | holding | cape | long_sleeves | looking_at_viewer | vial | black_skirt | bandaged_leg | black_choker | id_card | simple_background | thigh_strap | bag | test_tube | white_background | full_body | bow | smile | ears_through_headwear | bare_shoulders | black_headwear | short_sleeves | hat | official_alternate_costume | yellow_shirt | backpack | black_scarf | off_shoulder | standing | upper_body | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:--------|:----------|:-------|:---------------|:--------------------|:-------|:--------------|:---------------|:---------------|:----------|:--------------------|:--------------|:------|:------------|:-------------------|:------------|:------|:--------|:------------------------|:-----------------|:-----------------|:----------------|:------|:-----------------------------|:---------------|:-----------|:--------------|:---------------|:-----------|:-------------| | 0 | 33 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
vikp/doclaynet_processed
--- dataset_info: features: - name: image dtype: image - name: bboxes sequence: sequence: float64 - name: labels sequence: int64 - name: words sequence: string - name: split dtype: string splits: - name: train num_bytes: 32034973965.125 num_examples: 80863 download_size: 0 dataset_size: 32034973965.125 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "doclaynet_processed" Clean version of [DocLayNet](https://github.com/DS4SD/DocLayNet) ready for finetuning.
yiyic/clirmatrix
--- dataset_info: features: - name: text dtype: string - name: rate dtype: int64 - name: __index_level_0__ dtype: string splits: - name: de_en_multi8_test1 num_bytes: 1334400 num_examples: 1000 - name: de_fr_multi8_test1 num_bytes: 1336714 num_examples: 1000 - name: de_es_multi8_test1 num_bytes: 1336408 num_examples: 1000 - name: en_de_multi8_test1 num_bytes: 1146916 num_examples: 1000 - name: en_fr_multi8_test1 num_bytes: 1148710 num_examples: 1000 - name: en_es_multi8_test1 num_bytes: 1148404 num_examples: 1000 - name: es_en_multi8_test1 num_bytes: 1119660 num_examples: 1000 - name: es_fr_multi8_test1 num_bytes: 1121974 num_examples: 1000 - name: es_de_multi8_test1 num_bytes: 1120180 num_examples: 1000 - name: fr_en_multi8_test1 num_bytes: 1161002 num_examples: 1000 - name: fr_de_multi8_test1 num_bytes: 1161522 num_examples: 1000 - name: fr_es_multi8_test1 num_bytes: 1163010 num_examples: 1000 download_size: 8823803 dataset_size: 14298900 configs: - config_name: default data_files: - split: de_en_multi8_test1 path: data/de_en_multi8_test1-* - split: de_fr_multi8_test1 path: data/de_fr_multi8_test1-* - split: de_es_multi8_test1 path: data/de_es_multi8_test1-* - split: en_de_multi8_test1 path: data/en_de_multi8_test1-* - split: en_fr_multi8_test1 path: data/en_fr_multi8_test1-* - split: en_es_multi8_test1 path: data/en_es_multi8_test1-* - split: es_en_multi8_test1 path: data/es_en_multi8_test1-* - split: es_fr_multi8_test1 path: data/es_fr_multi8_test1-* - split: es_de_multi8_test1 path: data/es_de_multi8_test1-* - split: fr_en_multi8_test1 path: data/fr_en_multi8_test1-* - split: fr_de_multi8_test1 path: data/fr_de_multi8_test1-* - split: fr_es_multi8_test1 path: data/fr_es_multi8_test1-* ---
Mitsuki-Sakamoto/fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.1_seed_2_t_1.0_eval
--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string - name: gen_proxy_reward dtype: float64 - name: gen_gold_reward dtype: float64 splits: - name: epoch_0 num_bytes: 44053127 num_examples: 18928 - name: epoch_1 num_bytes: 44669586 num_examples: 18928 - name: epoch_10 num_bytes: 44730949 num_examples: 18928 - name: epoch_11 num_bytes: 44730605 num_examples: 18928 - name: epoch_12 num_bytes: 44730117 num_examples: 18928 - name: epoch_13 num_bytes: 44728361 num_examples: 18928 - name: epoch_14 num_bytes: 44730267 num_examples: 18928 - name: epoch_15 num_bytes: 44728443 num_examples: 18928 - name: epoch_16 num_bytes: 44728791 num_examples: 18928 - name: epoch_17 num_bytes: 44729768 num_examples: 18928 - name: epoch_18 num_bytes: 44729337 num_examples: 18928 - name: epoch_19 num_bytes: 44729952 num_examples: 18928 - name: epoch_2 num_bytes: 44733170 num_examples: 18928 - name: epoch_20 num_bytes: 44730371 num_examples: 18928 - name: epoch_21 num_bytes: 44730305 num_examples: 18928 - name: epoch_22 num_bytes: 44729540 num_examples: 18928 - name: epoch_23 num_bytes: 44729640 num_examples: 18928 - name: epoch_24 num_bytes: 44730718 num_examples: 18928 - name: epoch_25 num_bytes: 44731263 num_examples: 18928 - name: epoch_26 num_bytes: 44729373 num_examples: 18928 - name: epoch_27 num_bytes: 44729728 num_examples: 18928 - name: epoch_28 num_bytes: 44729738 num_examples: 18928 - name: epoch_29 num_bytes: 44729945 num_examples: 18928 - name: epoch_3 num_bytes: 44770625 num_examples: 18928 - name: epoch_4 num_bytes: 44776461 num_examples: 18928 - name: epoch_5 num_bytes: 44762728 num_examples: 18928 - name: epoch_6 num_bytes: 44749183 num_examples: 18928 - name: epoch_7 num_bytes: 44739230 num_examples: 18928 - name: epoch_8 num_bytes: 44733018 num_examples: 18928 - name: epoch_9 num_bytes: 44733757 num_examples: 18928 download_size: 710026115 dataset_size: 1341318096 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* --- # Dataset Card for "fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.1_seed_2_t_1.0_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
senhorsapo/nicole
--- license: openrail ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/fe795838
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1331 dataset_size: 180 --- # Dataset Card for "fe795838" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jiangyige/PSP5
--- license: unknown --- --- Description: The Paraphrased Sentence Pairs - 5 types (PSP-5) dataset comprises five fundamental categories of paraphrased English sentence pairs: 1. Declarative sentences (statements) 2. Interrogative sentences (questions) 3. Imperative sentences (commands) 4. Exclamatory sentences (exclamations) 5. Sentence fragments (oral English). There are 3 columns in the table: 1. sentence 2. chatGPT_paraphrased 3. type 10000 sentence pairs in all. ---
feedback-to-code/Server_Text_Dataset_1
--- license: apache-2.0 ---
yangwang825/sst2-pwws-2
--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: augment dtype: string splits: - name: train num_bytes: 2593835 num_examples: 20728 - name: validation num_bytes: 110096 num_examples: 872 - name: test num_bytes: 226340 num_examples: 1821 download_size: 1120309 dataset_size: 2930271 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
Anusha64/train-dataset-aeon
--- license: mit dataset_info: features: - name: instruction dtype: string - name: answer dtype: string splits: - name: train num_bytes: 70601 num_examples: 31 - name: validation num_bytes: 9705 num_examples: 5 - name: test num_bytes: 16493 num_examples: 7 download_size: 75537 dataset_size: 96799 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
JorangHorse/Third
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 1213654.0 num_examples: 2 download_size: 623252 dataset_size: 1213654.0 --- # Dataset Card for "Third" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ramunenavath/mydataset
--- license: openrail ---
joey234/mmlu-college_chemistry-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: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: neg_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string splits: - name: dev num_bytes: 7604 num_examples: 5 - name: test num_bytes: 807404 num_examples: 100 download_size: 137885 dataset_size: 815008 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-college_chemistry-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
darssanle/Primate_Dataset_With_Specifics
--- license: mit task_categories: - text-classification language: - en tags: - medical pretty_name: primate_dataset size_categories: - 10K<n<100K ---
helenqu/astro-classification-redshifts
--- license: mit tags: - time series - astrophysics - pretraining - connect-later size_categories: - 100K<n<1M --- # AstroClassification and Redshifts Datasets <!-- Provide a quick summary of the dataset. --> This dataset was used for the AstroClassification and Redshifts introduced in [Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations](). This is a dataset of simulated astronomical time-series (e.g., supernovae, active galactic nuclei), and the task is to classify the object type (AstroClassification) or predict the object's redshift (Redshifts). - **Repository:** https://github.com/helenqu/connect-later - **Paper:** will be updated - **Point of Contact: Helen Qu (<helenqu@sas.upenn.edu>)** ## 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. --> - **object_id**: unique object identifier - **times_wv**: 2D array of shape (N, 2) containing the observation times (modified Julian days, MJD) and filter (wavelength in nm) for each observation, N=number of observations - **lightcurve**: 2D array of shape (N, 2) containing the flux (arbitrary units) and flux error for each observation - **label**: integer representing the class of the object (see below for details) - **redshift**: redshift of the object ## Dataset Creation ### Source Data This is a modified version of the dataset from the 2018 Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) Kaggle competition The original Kaggle competition can be found [here](https://www.kaggle.com/c/PLAsTiCC-2018). [This note](https://arxiv.org/abs/1810.00001) from the competition describes the dataset in detail. Astronomers may be interested in [this paper](https://arxiv.org/abs/1903.11756) describing the simulations used to generate the data. - **Train**: 80% of the original PLAsTiCC training set augmented using the redshifting targeted augmentation described in the Connect Later paper - **Validation**: Remaining 20% of the original PLAsTiCC training set, *not* augmented or modified - **Test**: Subset of 10,000 objects randomly selected from the PLAsTiCC test set ### Object Types ``` 0: microlens-single 1: tidal disruption event (TDE) 2: eclipsing binary (EB) 3: type II supernova (SNII) 4: peculiar type Ia supernova (SNIax) 5: Mira variable 6: type Ibc supernova(SNIbc) 7: kilonova (KN) 8: M-dwarf 9: peculiar type Ia supernova (SNIa-91bg) 10: active galactic nuclei (AGN) 11: type Ia supernova (SNIa) 12: RR-Lyrae (RRL) 13: superluminous supernova (SLSN-I) 14: 5 "anomalous" types that are not present in training set: microlens-binary, intermediate luminosity optical transient (ILOT), calcium-rich transient (CaRT), pair instability supernova (PISN), microlens-string ``` ## Citation will be updated
ekolasky/DWIEForCustomLEDConsol
--- dataset_info: features: - name: input_ids sequence: int32 - name: result_labels sequence: int64 - name: grouping_vector sequence: sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 4305967 num_examples: 500 - name: validation num_bytes: 876462 num_examples: 96 download_size: 925707 dataset_size: 5182429 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
passionMan/mimic_tokenized_dataset_balanced_frac_0.1
--- dataset_info: features: - name: context dtype: string - name: label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 195080305 num_examples: 64761 - name: test num_bytes: 65034069 num_examples: 21588 download_size: 36831082 dataset_size: 260114374 --- # Dataset Card for "mimic_tokenized_dataset_balanced_frac_0.1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cmu-mlsp/librispeech960-encodec1024_asr
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - split: validation_other path: data/validation_other-* - split: test_other path: data/test_other-* dataset_info: features: - name: text dtype: string - name: audio_codes sequence: string - name: id dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 splits: - name: train num_bytes: 1859401929 num_examples: 281241 - name: validation num_bytes: 10515210 num_examples: 2703 - name: test num_bytes: 10516648 num_examples: 2620 - name: validation_other num_bytes: 9974741 num_examples: 2864 - name: test_other num_bytes: 10389123 num_examples: 2939 download_size: 0 dataset_size: 1900797651 --- # Dataset Card for "librispeech960-encodec1024_asr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bio-datasets/dft23-full
--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: answer_a dtype: string - name: answer_b dtype: string - name: answer_c dtype: string - name: answer_d dtype: string - name: answer_e dtype: string - name: correct_answers sequence: class_label: names: '0': a '1': b '2': c '3': d '4': e - name: subject_name dtype: string - name: number_correct_answers dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' splits: - name: train num_bytes: 1004721 num_examples: 2171 - name: validation num_bytes: 136786 num_examples: 312 - name: test num_bytes: 284765 num_examples: 622 download_size: 894075 dataset_size: 1426272 --- # Dataset Card for "dft23-full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mrpc_give_passive
--- 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: 14459 num_examples: 56 - name: train num_bytes: 27981 num_examples: 109 - name: validation num_bytes: 2653 num_examples: 10 download_size: 39594 dataset_size: 45093 --- # Dataset Card for "MULTI_VALUE_mrpc_give_passive" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chansung/llama2-stories
--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: title dtype: string - name: image dtype: string - name: story dtype: string splits: - name: train num_bytes: 4356500 num_examples: 73 download_size: 3539195 dataset_size: 4356500 ---
carnival13/rbrt_uda_lrg_ep5_2
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: domain_label dtype: int64 - name: pass_label dtype: int64 - name: input dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 1115662838 num_examples: 755110 download_size: 352431197 dataset_size: 1115662838 --- # Dataset Card for "rbrt_uda_lrg_ep5_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
imanmalhi/canada_realestate_listings
--- license: mit ---
open-llm-leaderboard/details_Sao10K__NyakuraV2.1-m7
--- pretty_name: Evaluation run of Sao10K/NyakuraV2.1-m7 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Sao10K/NyakuraV2.1-m7](https://huggingface.co/Sao10K/NyakuraV2.1-m7) 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_Sao10K__NyakuraV2.1-m7\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-12T04:30:54.576577](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__NyakuraV2.1-m7/blob/main/results_2023-12-12T04-30-54.576577.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\": 0.5812856791159661,\n\ \ \"acc_stderr\": 0.03351473539841468,\n \"acc_norm\": 0.5885734680789351,\n\ \ \"acc_norm_stderr\": 0.03422448074980651,\n \"mc1\": 0.29498164014687883,\n\ \ \"mc1_stderr\": 0.015964400965589664,\n \"mc2\": 0.45008851442315223,\n\ \ \"mc2_stderr\": 0.015144388624059283\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5511945392491467,\n \"acc_stderr\": 0.014534599585097662,\n\ \ \"acc_norm\": 0.5861774744027304,\n \"acc_norm_stderr\": 0.014392730009221007\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6320454092810197,\n\ \ \"acc_stderr\": 0.004812633280078261,\n \"acc_norm\": 0.8188607847042422,\n\ \ \"acc_norm_stderr\": 0.003843463792037909\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n\ \ \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.630188679245283,\n \"acc_stderr\": 0.02971142188010793,\n\ \ \"acc_norm\": 0.630188679245283,\n \"acc_norm_stderr\": 0.02971142188010793\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6041666666666666,\n\ \ \"acc_stderr\": 0.04089465449325582,\n \"acc_norm\": 0.6041666666666666,\n\ \ \"acc_norm_stderr\": 0.04089465449325582\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\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\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n\ \ \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n\ \ \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.03257901482099834,\n\ \ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.03257901482099834\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\"\ : 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\ : 0.6838709677419355,\n \"acc_stderr\": 0.026450874489042774,\n \"\ acc_norm\": 0.6838709677419355,\n \"acc_norm_stderr\": 0.026450874489042774\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"\ acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.0347769116216366,\n\ \ \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.0347769116216366\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.702020202020202,\n \"acc_stderr\": 0.03258630383836556,\n \"acc_norm\"\ : 0.702020202020202,\n \"acc_norm_stderr\": 0.03258630383836556\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8290155440414507,\n \"acc_stderr\": 0.027171213683164542,\n\ \ \"acc_norm\": 0.8290155440414507,\n \"acc_norm_stderr\": 0.027171213683164542\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5461538461538461,\n \"acc_stderr\": 0.025242770987126184,\n\ \ \"acc_norm\": 0.5461538461538461,\n \"acc_norm_stderr\": 0.025242770987126184\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652459,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652459\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.031968769891957786,\n\ \ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.031968769891957786\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659807,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659807\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.726605504587156,\n \"acc_stderr\": 0.019109299846098292,\n \"\ acc_norm\": 0.726605504587156,\n \"acc_norm_stderr\": 0.019109299846098292\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.03395322726375797,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.03395322726375797\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7205882352941176,\n \"acc_stderr\": 0.03149328104507957,\n \"\ acc_norm\": 0.7205882352941176,\n \"acc_norm_stderr\": 0.03149328104507957\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7172995780590717,\n \"acc_stderr\": 0.029312814153955924,\n \ \ \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.029312814153955924\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\ \ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\ \ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.04039314978724561,\n\ \ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.04039314978724561\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302871,\n \"acc_norm\"\ : 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302871\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615623,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615623\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\ \ \"acc_stderr\": 0.04742762361243011,\n \"acc_norm\": 0.5178571428571429,\n\ \ \"acc_norm_stderr\": 0.04742762361243011\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7905982905982906,\n\ \ \"acc_stderr\": 0.026655699653922737,\n \"acc_norm\": 0.7905982905982906,\n\ \ \"acc_norm_stderr\": 0.026655699653922737\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7624521072796935,\n\ \ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.7624521072796935,\n\ \ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.02572280220089581,\n\ \ \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.02572280220089581\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25139664804469275,\n\ \ \"acc_stderr\": 0.01450897945355397,\n \"acc_norm\": 0.25139664804469275,\n\ \ \"acc_norm_stderr\": 0.01450897945355397\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.027363593284684965,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.027363593284684965\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6591639871382636,\n\ \ \"acc_stderr\": 0.026920841260776165,\n \"acc_norm\": 0.6591639871382636,\n\ \ \"acc_norm_stderr\": 0.026920841260776165\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6512345679012346,\n \"acc_stderr\": 0.02651759772446501,\n\ \ \"acc_norm\": 0.6512345679012346,\n \"acc_norm_stderr\": 0.02651759772446501\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.42907801418439717,\n \"acc_stderr\": 0.02952591430255856,\n \ \ \"acc_norm\": 0.42907801418439717,\n \"acc_norm_stderr\": 0.02952591430255856\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.40547588005215124,\n\ \ \"acc_stderr\": 0.012539960672377202,\n \"acc_norm\": 0.40547588005215124,\n\ \ \"acc_norm_stderr\": 0.012539960672377202\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5661764705882353,\n \"acc_stderr\": 0.030105636570016633,\n\ \ \"acc_norm\": 0.5661764705882353,\n \"acc_norm_stderr\": 0.030105636570016633\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5931372549019608,\n \"acc_stderr\": 0.019873802005061177,\n \ \ \"acc_norm\": 0.5931372549019608,\n \"acc_norm_stderr\": 0.019873802005061177\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.673469387755102,\n \"acc_stderr\": 0.030021056238440307,\n\ \ \"acc_norm\": 0.673469387755102,\n \"acc_norm_stderr\": 0.030021056238440307\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.02619392354445414,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.02619392354445414\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036624,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036624\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.46987951807228917,\n\ \ \"acc_stderr\": 0.03885425420866766,\n \"acc_norm\": 0.46987951807228917,\n\ \ \"acc_norm_stderr\": 0.03885425420866766\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7543859649122807,\n \"acc_stderr\": 0.0330140594698725,\n\ \ \"acc_norm\": 0.7543859649122807,\n \"acc_norm_stderr\": 0.0330140594698725\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29498164014687883,\n\ \ \"mc1_stderr\": 0.015964400965589664,\n \"mc2\": 0.45008851442315223,\n\ \ \"mc2_stderr\": 0.015144388624059283\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7277032359905288,\n \"acc_stderr\": 0.012510697991453934\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2266868840030326,\n \ \ \"acc_stderr\": 0.011532758009339995\n }\n}\n```" repo_url: https://huggingface.co/Sao10K/NyakuraV2.1-m7 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|arc:challenge|25_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-12T04-30-54.576577.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|gsm8k|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hellaswag|10_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-12T04-30-54.576577.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-management|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T04-30-54.576577.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|truthfulqa:mc|0_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-12T04-30-54.576577.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_12T04_30_54.576577 path: - '**/details_harness|winogrande|5_2023-12-12T04-30-54.576577.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-12T04-30-54.576577.parquet' - config_name: results data_files: - split: 2023_12_12T04_30_54.576577 path: - results_2023-12-12T04-30-54.576577.parquet - split: latest path: - results_2023-12-12T04-30-54.576577.parquet --- # Dataset Card for Evaluation run of Sao10K/NyakuraV2.1-m7 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Sao10K/NyakuraV2.1-m7](https://huggingface.co/Sao10K/NyakuraV2.1-m7) 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_Sao10K__NyakuraV2.1-m7", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-12T04:30:54.576577](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__NyakuraV2.1-m7/blob/main/results_2023-12-12T04-30-54.576577.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": 0.5812856791159661, "acc_stderr": 0.03351473539841468, "acc_norm": 0.5885734680789351, "acc_norm_stderr": 0.03422448074980651, "mc1": 0.29498164014687883, "mc1_stderr": 0.015964400965589664, "mc2": 0.45008851442315223, "mc2_stderr": 0.015144388624059283 }, "harness|arc:challenge|25": { "acc": 0.5511945392491467, "acc_stderr": 0.014534599585097662, "acc_norm": 0.5861774744027304, "acc_norm_stderr": 0.014392730009221007 }, "harness|hellaswag|10": { "acc": 0.6320454092810197, "acc_stderr": 0.004812633280078261, "acc_norm": 0.8188607847042422, "acc_norm_stderr": 0.003843463792037909 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.630188679245283, "acc_stderr": 0.02971142188010793, "acc_norm": 0.630188679245283, "acc_norm_stderr": 0.02971142188010793 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6041666666666666, "acc_stderr": 0.04089465449325582, "acc_norm": 0.6041666666666666, "acc_norm_stderr": 0.04089465449325582 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "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": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4595744680851064, "acc_stderr": 0.03257901482099834, "acc_norm": 0.4595744680851064, "acc_norm_stderr": 0.03257901482099834 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6838709677419355, "acc_stderr": 0.026450874489042774, "acc_norm": 0.6838709677419355, "acc_norm_stderr": 0.026450874489042774 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7272727272727273, "acc_stderr": 0.0347769116216366, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.0347769116216366 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.702020202020202, "acc_stderr": 0.03258630383836556, "acc_norm": 0.702020202020202, "acc_norm_stderr": 0.03258630383836556 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8290155440414507, "acc_stderr": 0.027171213683164542, "acc_norm": 0.8290155440414507, "acc_norm_stderr": 0.027171213683164542 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5461538461538461, "acc_stderr": 0.025242770987126184, "acc_norm": 0.5461538461538461, "acc_norm_stderr": 0.025242770987126184 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3296296296296296, "acc_stderr": 0.02866120111652459, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.02866120111652459 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5882352941176471, "acc_stderr": 0.031968769891957786, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.031968769891957786 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.39072847682119205, "acc_stderr": 0.03983798306659807, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.03983798306659807 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.726605504587156, "acc_stderr": 0.019109299846098292, "acc_norm": 0.726605504587156, "acc_norm_stderr": 0.019109299846098292 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4537037037037037, "acc_stderr": 0.03395322726375797, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.03395322726375797 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7205882352941176, "acc_stderr": 0.03149328104507957, "acc_norm": 0.7205882352941176, "acc_norm_stderr": 0.03149328104507957 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7172995780590717, "acc_stderr": 0.029312814153955924, "acc_norm": 0.7172995780590717, "acc_norm_stderr": 0.029312814153955924 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6547085201793722, "acc_stderr": 0.03191100192835794, "acc_norm": 0.6547085201793722, "acc_norm_stderr": 0.03191100192835794 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6946564885496184, "acc_stderr": 0.04039314978724561, "acc_norm": 0.6946564885496184, "acc_norm_stderr": 0.04039314978724561 }, "harness|hendrycksTest-international_law|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302871, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302871 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497751, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497751 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7361963190184049, "acc_stderr": 0.03462419931615623, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.03462419931615623 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5178571428571429, "acc_stderr": 0.04742762361243011, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.04742762361243011 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7905982905982906, "acc_stderr": 0.026655699653922737, "acc_norm": 0.7905982905982906, "acc_norm_stderr": 0.026655699653922737 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7624521072796935, "acc_stderr": 0.015218733046150193, "acc_norm": 0.7624521072796935, "acc_norm_stderr": 0.015218733046150193 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6473988439306358, "acc_stderr": 0.02572280220089581, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.02572280220089581 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.25139664804469275, "acc_stderr": 0.01450897945355397, "acc_norm": 0.25139664804469275, "acc_norm_stderr": 0.01450897945355397 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6470588235294118, "acc_stderr": 0.027363593284684965, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.027363593284684965 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6591639871382636, "acc_stderr": 0.026920841260776165, "acc_norm": 0.6591639871382636, "acc_norm_stderr": 0.026920841260776165 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6512345679012346, "acc_stderr": 0.02651759772446501, "acc_norm": 0.6512345679012346, "acc_norm_stderr": 0.02651759772446501 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.42907801418439717, "acc_stderr": 0.02952591430255856, "acc_norm": 0.42907801418439717, "acc_norm_stderr": 0.02952591430255856 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.40547588005215124, "acc_stderr": 0.012539960672377202, "acc_norm": 0.40547588005215124, "acc_norm_stderr": 0.012539960672377202 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5661764705882353, "acc_stderr": 0.030105636570016633, "acc_norm": 0.5661764705882353, "acc_norm_stderr": 0.030105636570016633 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5931372549019608, "acc_stderr": 0.019873802005061177, "acc_norm": 0.5931372549019608, "acc_norm_stderr": 0.019873802005061177 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425465, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425465 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.673469387755102, "acc_stderr": 0.030021056238440307, "acc_norm": 0.673469387755102, "acc_norm_stderr": 0.030021056238440307 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.02619392354445414, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.02619392354445414 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.81, "acc_stderr": 0.03942772444036624, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036624 }, "harness|hendrycksTest-virology|5": { "acc": 0.46987951807228917, "acc_stderr": 0.03885425420866766, "acc_norm": 0.46987951807228917, "acc_norm_stderr": 0.03885425420866766 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7543859649122807, "acc_stderr": 0.0330140594698725, "acc_norm": 0.7543859649122807, "acc_norm_stderr": 0.0330140594698725 }, "harness|truthfulqa:mc|0": { "mc1": 0.29498164014687883, "mc1_stderr": 0.015964400965589664, "mc2": 0.45008851442315223, "mc2_stderr": 0.015144388624059283 }, "harness|winogrande|5": { "acc": 0.7277032359905288, "acc_stderr": 0.012510697991453934 }, "harness|gsm8k|5": { "acc": 0.2266868840030326, "acc_stderr": 0.011532758009339995 } } ``` ## 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.). 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nanina1/spider
--- license: mit ---
Englios/Wikipedia-Malaysian-Politicians
--- language: - en --- # Summary - wikipedia page : https://en.wikipedia.org/wiki/Category:Malaysian_politicians - Number of Politicians : 110 - Null Images of Politicians : 16 - link to dataset : https://huggingface.co/datasets/Englios/Wikipedia-Malaysian-Politicians - date of creation: 2024-20-01
shreyas1104/medical-intent-audio-dataset
--- dataset_info: features: - name: audio dtype: audio - name: label dtype: int64 splits: - name: train num_bytes: 5549933063.18 num_examples: 5895 - name: validation num_bytes: 295375977.0 num_examples: 385 - name: test num_bytes: 332239652.0 num_examples: 380 download_size: 4571560620 dataset_size: 6177548692.18 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
tmnam20/Vietnamese-News-raw
--- license: cc-by-4.0 task_categories: - text-generation language: - vi ---
CyberHarem/blue_pokemon
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of blue (Pokémon) This is the dataset of blue (Pokémon), containing 97 images and their tags. The core tags of this character are `brown_hair, green_eyes, spiked_hair`, 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 | 97 | 56.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blue_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 97 | 49.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blue_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 153 | 78.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blue_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 97 | 55.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blue_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 153 | 87.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blue_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/blue_pokemon', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1boy, holding_poke_ball, male_focus, solo, necklace, poke_ball_(basic), jacket, smile | | 1 | 5 | ![](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) | 1boy, bangs, grin, male_focus, necklace, short_hair, brown_eyes, long_sleeves, pokemon_(creature), purple_shirt, teeth, jacket, pants, poke_ball, boots, brown_footwear, holding, one_eye_closed, solo | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | holding_poke_ball | male_focus | solo | necklace | poke_ball_(basic) | jacket | smile | bangs | grin | short_hair | brown_eyes | long_sleeves | pokemon_(creature) | purple_shirt | teeth | pants | poke_ball | boots | brown_footwear | holding | one_eye_closed | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------------------|:-------------|:-------|:-----------|:--------------------|:---------|:--------|:--------|:-------|:-------------|:-------------|:---------------|:---------------------|:---------------|:--------|:--------|:------------|:--------|:-----------------|:----------|:-----------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
open-llm-leaderboard/details_222gate__TinyMistral-248Mx4-MOE
--- pretty_name: Evaluation run of 222gate/TinyMistral-248Mx4-MOE dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [222gate/TinyMistral-248Mx4-MOE](https://huggingface.co/222gate/TinyMistral-248Mx4-MOE)\ \ 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_222gate__TinyMistral-248Mx4-MOE\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-21T07:05:45.702729](https://huggingface.co/datasets/open-llm-leaderboard/details_222gate__TinyMistral-248Mx4-MOE/blob/main/results_2024-01-21T07-05-45.702729.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\": 0.24830542907208702,\n\ \ \"acc_stderr\": 0.030471240073543585,\n \"acc_norm\": 0.24917865866615294,\n\ \ \"acc_norm_stderr\": 0.03128580366341738,\n \"mc1\": 0.24357405140758873,\n\ \ \"mc1_stderr\": 0.01502635482491078,\n \"mc2\": 0.4865533579688347,\n\ \ \"mc2_stderr\": 0.01667138127210037\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.2235494880546075,\n \"acc_stderr\": 0.012174896631202607,\n\ \ \"acc_norm\": 0.295221843003413,\n \"acc_norm_stderr\": 0.013329750293382316\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2561242780322645,\n\ \ \"acc_stderr\": 0.00435599209003099,\n \"acc_norm\": 0.25712009559848636,\n\ \ \"acc_norm_stderr\": 0.004361529679492746\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n\ \ \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n\ \ \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.24342105263157895,\n \"acc_stderr\": 0.034923496688842384,\n\ \ \"acc_norm\": 0.24342105263157895,\n \"acc_norm_stderr\": 0.034923496688842384\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2830188679245283,\n \"acc_stderr\": 0.027724236492700904,\n\ \ \"acc_norm\": 0.2830188679245283,\n \"acc_norm_stderr\": 0.027724236492700904\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \"acc_norm\": 0.32,\n\ \ \"acc_norm_stderr\": 0.04688261722621503\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2774566473988439,\n\ \ \"acc_stderr\": 0.03414014007044036,\n \"acc_norm\": 0.2774566473988439,\n\ \ \"acc_norm_stderr\": 0.03414014007044036\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.044405219061793275,\n\ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.044405219061793275\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n\ \ \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.2127659574468085,\n \"acc_stderr\": 0.02675439134803976,\n\ \ \"acc_norm\": 0.2127659574468085,\n \"acc_norm_stderr\": 0.02675439134803976\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\ \ \"acc_stderr\": 0.0433913832257986,\n \"acc_norm\": 0.30701754385964913,\n\ \ \"acc_norm_stderr\": 0.0433913832257986\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n\ \ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2671957671957672,\n \"acc_stderr\": 0.022789673145776575,\n \"\ acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.022789673145776575\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n\ \ \"acc_stderr\": 0.03809523809523812,\n \"acc_norm\": 0.23809523809523808,\n\ \ \"acc_norm_stderr\": 0.03809523809523812\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.267741935483871,\n\ \ \"acc_stderr\": 0.025189006660212378,\n \"acc_norm\": 0.267741935483871,\n\ \ \"acc_norm_stderr\": 0.025189006660212378\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.22660098522167488,\n \"acc_stderr\": 0.02945486383529297,\n\ \ \"acc_norm\": 0.22660098522167488,\n \"acc_norm_stderr\": 0.02945486383529297\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.13,\n \"acc_stderr\": 0.0337997668989631,\n \"acc_norm\"\ : 0.13,\n \"acc_norm_stderr\": 0.0337997668989631\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.23636363636363636,\n \"acc_stderr\": 0.03317505930009179,\n\ \ \"acc_norm\": 0.23636363636363636,\n \"acc_norm_stderr\": 0.03317505930009179\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.20707070707070707,\n \"acc_stderr\": 0.028869778460267045,\n \"\ acc_norm\": 0.20707070707070707,\n \"acc_norm_stderr\": 0.028869778460267045\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.3471502590673575,\n \"acc_stderr\": 0.034356961683613546,\n\ \ \"acc_norm\": 0.3471502590673575,\n \"acc_norm_stderr\": 0.034356961683613546\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2743589743589744,\n \"acc_stderr\": 0.022622765767493214,\n\ \ \"acc_norm\": 0.2743589743589744,\n \"acc_norm_stderr\": 0.022622765767493214\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712173,\n \ \ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712173\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.2815126050420168,\n \"acc_stderr\": 0.029213549414372146,\n\ \ \"acc_norm\": 0.2815126050420168,\n \"acc_norm_stderr\": 0.029213549414372146\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.271523178807947,\n \"acc_stderr\": 0.036313298039696545,\n \"\ acc_norm\": 0.271523178807947,\n \"acc_norm_stderr\": 0.036313298039696545\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.3100917431192661,\n \"acc_stderr\": 0.01983084968443975,\n \"\ acc_norm\": 0.3100917431192661,\n \"acc_norm_stderr\": 0.01983084968443975\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.20833333333333334,\n \"acc_stderr\": 0.027696910713093936,\n \"\ acc_norm\": 0.20833333333333334,\n \"acc_norm_stderr\": 0.027696910713093936\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.23039215686274508,\n \"acc_stderr\": 0.029554292605695046,\n \"\ acc_norm\": 0.23039215686274508,\n \"acc_norm_stderr\": 0.029554292605695046\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.25738396624472576,\n \"acc_stderr\": 0.0284588209914603,\n \ \ \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.0284588209914603\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.19282511210762332,\n\ \ \"acc_stderr\": 0.02647824096048936,\n \"acc_norm\": 0.19282511210762332,\n\ \ \"acc_norm_stderr\": 0.02647824096048936\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.039153454088478354,\n\ \ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.039153454088478354\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2066115702479339,\n \"acc_stderr\": 0.03695980128098825,\n \"\ acc_norm\": 0.2066115702479339,\n \"acc_norm_stderr\": 0.03695980128098825\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2037037037037037,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.2037037037037037,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.24539877300613497,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.24539877300613497,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.040598672469526864,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.040598672469526864\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.32038834951456313,\n \"acc_stderr\": 0.0462028408228004,\n\ \ \"acc_norm\": 0.32038834951456313,\n \"acc_norm_stderr\": 0.0462028408228004\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.20512820512820512,\n\ \ \"acc_stderr\": 0.026453508054040356,\n \"acc_norm\": 0.20512820512820512,\n\ \ \"acc_norm_stderr\": 0.026453508054040356\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.14,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.14,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\ \ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\ \ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.25722543352601157,\n \"acc_stderr\": 0.02353292543104429,\n\ \ \"acc_norm\": 0.25722543352601157,\n \"acc_norm_stderr\": 0.02353292543104429\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.264804469273743,\n\ \ \"acc_stderr\": 0.01475690648326066,\n \"acc_norm\": 0.264804469273743,\n\ \ \"acc_norm_stderr\": 0.01475690648326066\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.02555316999182653,\n\ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.02555316999182653\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.19935691318327975,\n\ \ \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.19935691318327975,\n\ \ \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2345679012345679,\n \"acc_stderr\": 0.02357688174400572,\n\ \ \"acc_norm\": 0.2345679012345679,\n \"acc_norm_stderr\": 0.02357688174400572\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.24113475177304963,\n \"acc_stderr\": 0.025518731049537762,\n \ \ \"acc_norm\": 0.24113475177304963,\n \"acc_norm_stderr\": 0.025518731049537762\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2588005215123859,\n\ \ \"acc_stderr\": 0.01118610904656461,\n \"acc_norm\": 0.2588005215123859,\n\ \ \"acc_norm_stderr\": 0.01118610904656461\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.3272058823529412,\n \"acc_stderr\": 0.028501452860396563,\n\ \ \"acc_norm\": 0.3272058823529412,\n \"acc_norm_stderr\": 0.028501452860396563\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.24673202614379086,\n \"acc_stderr\": 0.0174408203674025,\n \ \ \"acc_norm\": 0.24673202614379086,\n \"acc_norm_stderr\": 0.0174408203674025\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2909090909090909,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.2909090909090909,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.27755102040816326,\n \"acc_stderr\": 0.02866685779027465,\n\ \ \"acc_norm\": 0.27755102040816326,\n \"acc_norm_stderr\": 0.02866685779027465\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2537313432835821,\n\ \ \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.2537313432835821,\n\ \ \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.21084337349397592,\n\ \ \"acc_stderr\": 0.031755547866299194,\n \"acc_norm\": 0.21084337349397592,\n\ \ \"acc_norm_stderr\": 0.031755547866299194\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.27485380116959063,\n \"acc_stderr\": 0.03424042924691582,\n\ \ \"acc_norm\": 0.27485380116959063,\n \"acc_norm_stderr\": 0.03424042924691582\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.24357405140758873,\n\ \ \"mc1_stderr\": 0.01502635482491078,\n \"mc2\": 0.4865533579688347,\n\ \ \"mc2_stderr\": 0.01667138127210037\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5177584846093133,\n \"acc_stderr\": 0.014043619596174962\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/222gate/TinyMistral-248Mx4-MOE 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_21T07_05_45.702729 path: - '**/details_harness|arc:challenge|25_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-21T07-05-45.702729.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|gsm8k|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hellaswag|10_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-21T07-05-45.702729.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-management|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T07-05-45.702729.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|truthfulqa:mc|0_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-21T07-05-45.702729.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_21T07_05_45.702729 path: - '**/details_harness|winogrande|5_2024-01-21T07-05-45.702729.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-21T07-05-45.702729.parquet' - config_name: results data_files: - split: 2024_01_21T07_05_45.702729 path: - results_2024-01-21T07-05-45.702729.parquet - split: latest path: - results_2024-01-21T07-05-45.702729.parquet --- # Dataset Card for Evaluation run of 222gate/TinyMistral-248Mx4-MOE <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [222gate/TinyMistral-248Mx4-MOE](https://huggingface.co/222gate/TinyMistral-248Mx4-MOE) 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_222gate__TinyMistral-248Mx4-MOE", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-21T07:05:45.702729](https://huggingface.co/datasets/open-llm-leaderboard/details_222gate__TinyMistral-248Mx4-MOE/blob/main/results_2024-01-21T07-05-45.702729.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": 0.24830542907208702, "acc_stderr": 0.030471240073543585, "acc_norm": 0.24917865866615294, "acc_norm_stderr": 0.03128580366341738, "mc1": 0.24357405140758873, "mc1_stderr": 0.01502635482491078, "mc2": 0.4865533579688347, "mc2_stderr": 0.01667138127210037 }, "harness|arc:challenge|25": { "acc": 0.2235494880546075, "acc_stderr": 0.012174896631202607, "acc_norm": 0.295221843003413, "acc_norm_stderr": 0.013329750293382316 }, "harness|hellaswag|10": { "acc": 0.2561242780322645, "acc_stderr": 0.00435599209003099, "acc_norm": 0.25712009559848636, "acc_norm_stderr": 0.004361529679492746 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.23703703703703705, "acc_stderr": 0.03673731683969506, "acc_norm": 0.23703703703703705, "acc_norm_stderr": 0.03673731683969506 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.24342105263157895, "acc_stderr": 0.034923496688842384, "acc_norm": 0.24342105263157895, "acc_norm_stderr": 0.034923496688842384 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2830188679245283, "acc_stderr": 0.027724236492700904, "acc_norm": 0.2830188679245283, "acc_norm_stderr": 0.027724236492700904 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03476590104304134, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2774566473988439, "acc_stderr": 0.03414014007044036, "acc_norm": 0.2774566473988439, "acc_norm_stderr": 0.03414014007044036 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.27450980392156865, "acc_stderr": 0.044405219061793275, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.044405219061793275 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.19, "acc_stderr": 0.03942772444036623, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036623 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2127659574468085, "acc_stderr": 0.02675439134803976, "acc_norm": 0.2127659574468085, "acc_norm_stderr": 0.02675439134803976 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.30701754385964913, "acc_stderr": 0.0433913832257986, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.0433913832257986 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2689655172413793, "acc_stderr": 0.03695183311650232, "acc_norm": 0.2689655172413793, "acc_norm_stderr": 0.03695183311650232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2671957671957672, "acc_stderr": 0.022789673145776575, "acc_norm": 0.2671957671957672, "acc_norm_stderr": 0.022789673145776575 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.23809523809523808, "acc_stderr": 0.03809523809523812, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.03809523809523812 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.267741935483871, "acc_stderr": 0.025189006660212378, "acc_norm": 0.267741935483871, "acc_norm_stderr": 0.025189006660212378 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.22660098522167488, "acc_stderr": 0.02945486383529297, "acc_norm": 0.22660098522167488, "acc_norm_stderr": 0.02945486383529297 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.13, "acc_stderr": 0.0337997668989631, "acc_norm": 0.13, "acc_norm_stderr": 0.0337997668989631 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.23636363636363636, "acc_stderr": 0.03317505930009179, "acc_norm": 0.23636363636363636, "acc_norm_stderr": 0.03317505930009179 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.20707070707070707, "acc_stderr": 0.028869778460267045, "acc_norm": 0.20707070707070707, "acc_norm_stderr": 0.028869778460267045 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.3471502590673575, "acc_stderr": 0.034356961683613546, "acc_norm": 0.3471502590673575, "acc_norm_stderr": 0.034356961683613546 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2743589743589744, "acc_stderr": 0.022622765767493214, "acc_norm": 0.2743589743589744, "acc_norm_stderr": 0.022622765767493214 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25925925925925924, "acc_stderr": 0.026719240783712173, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.026719240783712173 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.2815126050420168, "acc_stderr": 0.029213549414372146, "acc_norm": 0.2815126050420168, "acc_norm_stderr": 0.029213549414372146 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.271523178807947, "acc_stderr": 0.036313298039696545, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.036313298039696545 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.3100917431192661, "acc_stderr": 0.01983084968443975, "acc_norm": 0.3100917431192661, "acc_norm_stderr": 0.01983084968443975 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.20833333333333334, "acc_stderr": 0.027696910713093936, "acc_norm": 0.20833333333333334, "acc_norm_stderr": 0.027696910713093936 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.23039215686274508, "acc_stderr": 0.029554292605695046, "acc_norm": 0.23039215686274508, "acc_norm_stderr": 0.029554292605695046 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.25738396624472576, "acc_stderr": 0.0284588209914603, "acc_norm": 0.25738396624472576, "acc_norm_stderr": 0.0284588209914603 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.19282511210762332, "acc_stderr": 0.02647824096048936, "acc_norm": 0.19282511210762332, "acc_norm_stderr": 0.02647824096048936 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2748091603053435, "acc_stderr": 0.039153454088478354, "acc_norm": 0.2748091603053435, "acc_norm_stderr": 0.039153454088478354 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2066115702479339, "acc_stderr": 0.03695980128098825, "acc_norm": 0.2066115702479339, "acc_norm_stderr": 0.03695980128098825 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.2037037037037037, "acc_stderr": 0.03893542518824847, "acc_norm": 0.2037037037037037, "acc_norm_stderr": 0.03893542518824847 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.24539877300613497, "acc_stderr": 0.03380939813943354, "acc_norm": 0.24539877300613497, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.24107142857142858, "acc_stderr": 0.040598672469526864, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.040598672469526864 }, "harness|hendrycksTest-management|5": { "acc": 0.32038834951456313, "acc_stderr": 0.0462028408228004, "acc_norm": 0.32038834951456313, "acc_norm_stderr": 0.0462028408228004 }, "harness|hendrycksTest-marketing|5": { "acc": 0.20512820512820512, "acc_stderr": 0.026453508054040356, "acc_norm": 0.20512820512820512, "acc_norm_stderr": 0.026453508054040356 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.14, "acc_stderr": 0.0348735088019777, "acc_norm": 0.14, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.23754789272030652, "acc_stderr": 0.015218733046150193, "acc_norm": 0.23754789272030652, "acc_norm_stderr": 0.015218733046150193 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.25722543352601157, "acc_stderr": 0.02353292543104429, "acc_norm": 0.25722543352601157, "acc_norm_stderr": 0.02353292543104429 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.264804469273743, "acc_stderr": 0.01475690648326066, "acc_norm": 0.264804469273743, "acc_norm_stderr": 0.01475690648326066 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.27450980392156865, "acc_stderr": 0.02555316999182653, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.02555316999182653 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.19935691318327975, "acc_stderr": 0.022691033780549656, "acc_norm": 0.19935691318327975, "acc_norm_stderr": 0.022691033780549656 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2345679012345679, "acc_stderr": 0.02357688174400572, "acc_norm": 0.2345679012345679, "acc_norm_stderr": 0.02357688174400572 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.24113475177304963, "acc_stderr": 0.025518731049537762, "acc_norm": 0.24113475177304963, "acc_norm_stderr": 0.025518731049537762 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2588005215123859, "acc_stderr": 0.01118610904656461, "acc_norm": 0.2588005215123859, "acc_norm_stderr": 0.01118610904656461 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.3272058823529412, "acc_stderr": 0.028501452860396563, "acc_norm": 0.3272058823529412, "acc_norm_stderr": 0.028501452860396563 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.24673202614379086, "acc_stderr": 0.0174408203674025, "acc_norm": 0.24673202614379086, "acc_norm_stderr": 0.0174408203674025 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2909090909090909, "acc_stderr": 0.04350271442923243, "acc_norm": 0.2909090909090909, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.27755102040816326, "acc_stderr": 0.02866685779027465, "acc_norm": 0.27755102040816326, "acc_norm_stderr": 0.02866685779027465 }, "harness|hendrycksTest-sociology|5": { "acc": 0.2537313432835821, "acc_stderr": 0.03076944496729602, "acc_norm": 0.2537313432835821, "acc_norm_stderr": 0.03076944496729602 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.18, "acc_stderr": 0.03861229196653694, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-virology|5": { "acc": 0.21084337349397592, "acc_stderr": 0.031755547866299194, "acc_norm": 0.21084337349397592, "acc_norm_stderr": 0.031755547866299194 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.27485380116959063, "acc_stderr": 0.03424042924691582, "acc_norm": 0.27485380116959063, "acc_norm_stderr": 0.03424042924691582 }, "harness|truthfulqa:mc|0": { "mc1": 0.24357405140758873, "mc1_stderr": 0.01502635482491078, "mc2": 0.4865533579688347, "mc2_stderr": 0.01667138127210037 }, "harness|winogrande|5": { "acc": 0.5177584846093133, "acc_stderr": 0.014043619596174962 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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]
russellsheep/drawbench-upsampled-zephyr-7b-alpha
--- dataset_info: features: - name: Prompt dtype: string - name: Upsampled Prompt dtype: string - name: Category dtype: string splits: - name: train num_bytes: 88284 num_examples: 200 download_size: 52827 dataset_size: 88284 configs: - config_name: default data_files: - split: train path: data/train-* ---
thercyl/BRK
--- dataset_info: features: - name: 'Unnamed: 0' dtype: float64 - name: Ticker dtype: string - name: Year dtype: string - name: Text dtype: string - name: Embedding dtype: string splits: - name: train num_bytes: 60382282 num_examples: 1731 download_size: 38342637 dataset_size: 60382282 --- # Dataset Card for "BRK" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
derexHf/MathInstructTop2K
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 5591391 num_examples: 2000 download_size: 2494977 dataset_size: 5591391 configs: - config_name: default data_files: - split: train path: data/train-* ---
Falah/catalogue_photography_prompts
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 114345 num_examples: 1000 download_size: 2050 dataset_size: 114345 --- # Dataset Card for "catalogue_photography_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
premio-ai/TheArabicPile_Poetry
--- language: - ar license: cc-by-nc-4.0 task_categories: - text-generation dataset_info: - config_name: dedup features: - name: text dtype: string splits: - name: train num_bytes: 311172546 num_examples: 61085 download_size: 154601576 dataset_size: 311172546 - config_name: original features: - name: text dtype: string splits: - name: train num_bytes: 287936720 num_examples: 61591 download_size: 152946428 dataset_size: 287936720 configs: - config_name: dedup data_files: - split: train path: dedup/train-* - config_name: original data_files: - split: train path: data/train-* --- # The Arabic Pile ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64da0fd923557cdce3e514c3/J0oY67lVvecV75SOlWpjc.png) ## Introduction: The Arabic Pile is a comprehensive dataset meticulously designed to parallel the structure of The Pile and The Nordic Pile. Focused on the Arabic language, the dataset encompasses a vast array of linguistic nuances, incorporating both Modern Standard Arabic (MSA) and various Levantine, North African, and Egyptian dialects. Tailored for the training and fine-tuning of large language models, the dataset consists of 13 subsets, each uniquely crafted to cater to different linguistic domains. ## The Poetry Subset: This dataset has a collection of Arabic poetry. ## Other Subsets: 1. premio-ai/TheArabicPile 2. premio-ai/TheArabicPile_Web 3. premio-ai/TheArabicPile_Lyrics 4. premio-ai/TheArabicPile_Reviews 5. premio-ai/TheArabicPile_Dialects 6. premio-ai/TheArabicPile_Mathematics 7. premio-ai/TheArabicPile_Conversational 8. premio-ai/TheArabicPile_Articles 9. premio-ai/TheArabicPile_Poetry 10. premio-ai/TheArabicPile_Medical 11. premio-ai/TheArabicPile_Miscellaneous 12. premio-ai/TheArabicPile_SocialMedia 13. premio-ai/TheArabicPile_Translations 14. premio-ai/TheArabicPile_Books These subsets serve distinct purposes, ranging from mathematical content to conversational dialogue, medical texts, and more. Notably, there's a dedicated subset, "premio-ai/TheArabicPile_SocialMedia," emphasizing the inclusion of language commonly found in social media contexts. ## Dataset Description * Curated by: Premio.AI team * Language(s) (NLP): Arabic, multiple languages on the translation dataset. * License: CC BY-NC 4.0 Deed - Non Commercial. * For any commercial uses or licensing, please contact mo@premio.ai. ## Data Structure The datasets are divided into two main subsets: 1. Original Subset: The raw data as collected from sources, without modifications. 2. Deduplication Subset: A filtered and cleaned version, enhancing usability for large language models by reducing redundancy and noise. The Arabic Pile extends an invitation not only for training and fine-tuning large language models but also for diverse applications across linguistic domains. Whether for research, analysis, or other linguistic endeavors, The Arabic Pile stands as a rich resource for the exploration of Arabic language intricacies. ## Data Collection Please refer to the paper for more details on our data collection procedures. ## Data Format The dataset has one single column called text. The text should contain the required meta data and the body combined. This was done to make sure that it will be a good fit for direct training or fine-tuning of large language models. Please note that the meta data might require to be repeated if your training context window won’t fit the entire body of text. ## Potential Bias As with any large-scale dataset, The Arabic Pile is not immune to potential biases that may influence the training and performance of language models. It's crucial to transparently address these biases to ensure responsible usage and interpretation of the dataset. Here are some considerations: 1. Dialectal Imbalance: The dataset incorporates various Arabic dialects, with a focus on Levantine, North African, and Egyptian variants. However, there might be variations in the representation of these dialects, potentially leading to an imbalance in the training data. 2. Source Influence: Bias may arise from the sources of the original data. The dataset collects information from diverse platforms and domains, and biases inherent in those sources could transfer to the dataset. 3. Social Media Context: Some of our datasets have language from social media platforms and online platforms. This subset may introduce biases inherent in online discourse, such as informal language, colloquial expressions, and potential subjectivity in politics, religion or culture. 4. Genre and Domain Bias: Different subsets cater to distinct linguistic domains, such as medical texts, poetry, reviews, and more. Each domain carries its own linguistic characteristics, potentially leading to biases based on the genres represented. ## License Information for The Arabic Pile: No Commercial Use The Arabic Pile is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). This license is designed to facilitate the open sharing and collaboration of the dataset while ensuring responsible and non-commercial usage. Key Points of the License: * Attribution (BY): Users are free to share, adapt, and build upon the dataset, even commercially, as long as they provide appropriate attribution to the dataset creators. * Non-Commercial (NC): The dataset may not be used for commercial purposes. Any use for commercial gain requires explicit permission from the dataset creators. * No Additional Restrictions: The license allows for maximum freedom of use, provided the terms of attribution and non-commercial use are adhered to. How to Cite: When using The Arabic Pile in your work, please include a proper citation to acknowledge the dataset creators. A recommended citation can be found in the model card for easy reference. License Deed: For a comprehensive understanding of the terms and conditions, please refer to the CC BY-NC 4.0 License Deed. By adopting this license, we aim to foster a collaborative and open environment for the exploration and advancement of Arabic language understanding and natural language processing. ## Citation When utilizing The Arabic Pile in your research, development, or other projects, we kindly request that you cite the dataset using the following format: @article{alrefaie2024arabicpile, author = {Mohamed Taher Alrefaie, Mahmoud Ibrahim Barbary, Ahmed Yasser Hassanein, Shiref Khaled Elhalawany, Karim Ashraf Elsayed, Ahmed Yasser }, title = {The Arabic Pile: A Large Scale Dataset of Diverse Text for Large Language Modeling}, year = {2024}, url = {https://huggingface.co/datasets/premio-ai/TheArabicPile} }
iwecht/hard_captions
--- dataset_info: features: - name: annID dtype: int64 - name: caption dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 364027 num_examples: 5000 download_size: 200465 dataset_size: 364027 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "hard_captions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
confit/wmms
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: species dtype: string - name: label dtype: class_label: names: '0': Atlantic_Spotted_Dolphin '1': Bearded_Seal '2': Beluga,_White_Whale '3': Bottlenose_Dolphin '4': Bowhead_Whale '5': Clymene_Dolphin '6': Common_Dolphin '7': False_Killer_Whale '8': Fin,_Finback_Whale '9': Frasers_Dolphin '10': Grampus,_Rissos_Dolphin '11': Harp_Seal '12': Humpback_Whale '13': Killer_Whale '14': Leopard_Seal '15': Long-Finned_Pilot_Whale '16': Melon_Headed_Whale '17': Minke_Whale '18': Narwhal '19': Northern_Right_Whale '20': Pantropical_Spotted_Dolphin '21': Ross_Seal '22': Rough-Toothed_Dolphin '23': Short-Finned_Pacific_Pilot_Whale '24': Southern_Right_Whale '25': Sperm_Whale '26': Spinner_Dolphin '27': Striped_Dolphin '28': Walrus '29': Weddell_Seal '30': White-beaked_Dolphin '31': White-sided_Dolphin splits: - name: train num_bytes: 1179470284 num_examples: 1357 - name: test num_bytes: 154350686 num_examples: 340 download_size: 1217429434 dataset_size: 1333820970 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* task_categories: - audio-classification tags: - multiclass size_categories: - 1K<n<10K --- # Watkins Marine Mammal Sound (WMMS) Database Sound files on this website are free to download for personal or academic (not commercial) use. Sound files and associated metadata are credited as follows: "Watkins Marine Mammal Sound Database, Woods Hole Oceanographic Institution and the New Bedford Whaling Museum." Database could be found and downloaded from [here](https://archive.org/details/watkins_202104). In this database version, the audio archive includes sounds of 32 species: - Atlantic_Spotted_Dolphin - Bearded_Seal - Beluga,_White_Whale - Bottlenose_Dolphin - Bowhead_Whale - Clymene_Dolphin - Common_Dolphin - False_Killer_Whale - Fin,_Finback_Whale - Frasers_Dolphin - Grampus,_Rissos_Dolphin - Harp_Seal - Humpback_Whale - Killer_Whale - Leopard_Seal - Long-Finned_Pilot_Whale - Melon_Headed_Whale - Minke_Whale - Narwhal - Northern_Right_Whale - Pantropical_Spotted_Dolphin - Ross_Seal - Rough-Toothed_Dolphin - Short-Finned_Pacific_Pilot_Whale - Southern_Right_Whale - Sperm_Whale - Spinner_Dolphin - Striped_Dolphin - Walrus - Weddell_Seal - White-beaked_Dolphin - White-sided_Dolphin Since there is no official train/test split, we use 80% of the samples for training (1357) and the rest for testing (340).
RogerB/kin_en_DigitalUmuganda
--- dataset_info: features: - name: rw dtype: string - name: en dtype: string splits: - name: train num_bytes: 4550456 num_examples: 47824 download_size: 2836819 dataset_size: 4550456 --- # Dataset Card for "kin_en_DigitalUmuganda" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) # Dataset Information The dataset was created by [DigitalUmuganda](https://huggingface.co/datasets/DigitalUmuganda/kinyarwanda-english-machine-translation-dataset/tree/main) for machine translation from Kinyarwanda to English
beta-reduction/webcrawl-202401
--- license: cc-by-sa-3.0 ---
zjysteven/WikiMIA_paraphrased_perturbed
--- dataset_info: features: - name: input dtype: string - name: label dtype: int64 splits: - name: WikiMIA_length32_paraphrased num_bytes: 163365 num_examples: 776 - name: WikiMIA_length64_paraphrased num_bytes: 224644 num_examples: 542 - name: WikiMIA_length128_paraphrased num_bytes: 206645 num_examples: 250 - name: WikiMIA_length32_perturbed num_bytes: 1650773 num_examples: 7760 - name: WikiMIA_length64_perturbed num_bytes: 2255354 num_examples: 5420 - name: WikiMIA_length128_perturbed num_bytes: 2092896 num_examples: 2500 - name: WikiMIA_length32_paraphrased_perturbed num_bytes: 1662467 num_examples: 7760 - name: WikiMIA_length64_paraphrased_perturbed num_bytes: 2286059 num_examples: 5420 - name: WikiMIA_length128_paraphrased_perturbed num_bytes: 2105242 num_examples: 2500 download_size: 3282711 dataset_size: 12647445 configs: - config_name: default data_files: - split: WikiMIA_length32_paraphrased path: data/WikiMIA_length32_paraphrased-* - split: WikiMIA_length64_paraphrased path: data/WikiMIA_length64_paraphrased-* - split: WikiMIA_length128_paraphrased path: data/WikiMIA_length128_paraphrased-* - split: WikiMIA_length32_perturbed path: data/WikiMIA_length32_perturbed-* - split: WikiMIA_length64_perturbed path: data/WikiMIA_length64_perturbed-* - split: WikiMIA_length128_perturbed path: data/WikiMIA_length128_perturbed-* - split: WikiMIA_length32_paraphrased_perturbed path: data/WikiMIA_length32_paraphrased_perturbed-* - split: WikiMIA_length64_paraphrased_perturbed path: data/WikiMIA_length64_paraphrased_perturbed-* - split: WikiMIA_length128_paraphrased_perturbed path: data/WikiMIA_length128_paraphrased_perturbed-* license: mit --- ## 📘 WikiMIA paraphrased and perturbed versions The WikiMIA dataset serves as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from extensive large language models. It is originally constructed by Shi et al. (see the [original data repo](https://huggingface.co/datasets/swj0419/WikiMIA) for more details). - The authors studied a *paraphrased* setting in their paper, where instead of detecting verbatim training texts, the goal is to detect (slightly) paraphrased version. Unfortunately they didn't release such data splits. Here we provide our paraphrased version, which is obtained by instructing ChatGPT to replace certain number of words without changing the original semantic meaning. - We further provide perturbed versions of WikiMIA, which are necessary to run the Neighbor attack. Perturbed versions are obtained by perturbing each input sentence with masked language model. For each input we have perturbed 10 times so you don't have to repeat this process yourself (which can be time consuming). ## 💻 Loading the datasets To load the dataset: ```python from datasets import load_dataset LENGTH = 32 SPLIT_NAME = "paraphrased" dataset = load_dataset("zjysteven/WikiMIA_paraphrased_perturbed", split=f"WikiMIA_length{LENGTH}_{SPLIT_NAME}") ``` * LENGTH: choose from `32, 64, 128`, which is the length of the input text. * SPLIT_NAME: choose from `"paraphrased", "perturbed", "paraphrased_perturbed"`. * *Label 0*: Refers to the unseen (non-training) data during pretraining. *Label 1*: Refers to the seen (training) data. ## 🛠️ Codebase For more details on evaluating multiple MIA methods on these WikiMIA datasets, visit our [GitHub repository](https://github.com/zjysteven/mink-plus-plus), where we also propose a novel method, **Min-K%++**, that significantly outperforms both the Min-K% by Shi et al. and other baseline methods. ## ⭐ Citing our Work If you find our codebase and datasets beneficial, kindly cite our work and the original WikiMIA: ```bibtex @misc{zhang2024mink, title={Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models}, author={Jingyang Zhang and Jingwei Sun and Eric Yeats and Yang Ouyang and Martin Kuo and Jianyi Zhang and Hao Yang and Hai Li}, year={2024}, eprint={2404.02936}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{ shi2024detecting, title={Detecting Pretraining Data from Large Language Models}, author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=zWqr3MQuNs} } ```
seansullivan/automation-txt
--- license: other license_name: me license_link: LICENSE ---
kevinassogba/frbam-llama2
--- license: mit dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 898028 num_examples: 3000 - name: test num_bytes: 300470 num_examples: 1000 - name: val num_bytes: 317907 num_examples: 1000 download_size: 981949 dataset_size: 1516405 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: val path: data/val-* ---
CyberHarem/sumire_bluearchive
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sumire/乙花スミレ/菫 (Blue Archive) This is the dataset of sumire/乙花スミレ/菫 (Blue Archive), containing 92 images and their tags. The core tags of this character are `long_hair, ponytail, breasts, black_hair, purple_eyes, hair_ornament, hair_flower, scrunchie, purple_scrunchie, very_long_hair, hair_scrunchie, large_breasts, halo, sidelocks, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 92 | 141.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sumire_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 92 | 119.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sumire_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 238 | 244.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sumire_bluearchive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/sumire_bluearchive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 7 | ![](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, bare_shoulders, black_pants, crop_top, flower, green_choker, midriff, navel, solo, stomach, white_sports_bra, cowboy_shot, looking_at_viewer, yoga_pants, cleavage, simple_background, blush, bottle, closed_mouth, high_ponytail, holding_towel, parted_lips, sweatband, white_background, wristband | | 1 | 5 | ![](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, black_pants, blush, eyewear_hang, flower, long_sleeves, looking_at_viewer, midriff, open_jacket, solo, white_sports_bra, yoga_pants, bare_shoulders, collarbone, green_choker, navel, off_shoulder, stomach, sunglasses, unworn_eyewear, white_jacket, cleavage, cowboy_shot, open_mouth, parted_lips, standing, sweat, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_pants | crop_top | flower | green_choker | midriff | navel | solo | stomach | white_sports_bra | cowboy_shot | looking_at_viewer | yoga_pants | cleavage | simple_background | blush | bottle | closed_mouth | high_ponytail | holding_towel | parted_lips | sweatband | white_background | wristband | eyewear_hang | long_sleeves | open_jacket | collarbone | off_shoulder | sunglasses | unworn_eyewear | white_jacket | open_mouth | standing | sweat | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------|:-----------|:---------|:---------------|:----------|:--------|:-------|:----------|:-------------------|:--------------|:--------------------|:-------------|:-----------|:--------------------|:--------|:---------|:---------------|:----------------|:----------------|:--------------|:------------|:-------------------|:------------|:---------------|:---------------|:--------------|:-------------|:---------------|:-------------|:-----------------|:---------------|:-------------|:-----------|:--------| | 0 | 7 | ![](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 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | X | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X |
Bsbell21/MarketMailAI180
--- dataset_info: features: - name: product dtype: string - name: description dtype: string - name: marketing_email dtype: string splits: - name: train num_bytes: 109776.75 num_examples: 135 - name: test num_bytes: 36592.25 num_examples: 45 download_size: 89875 dataset_size: 146369.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
distil-whisper/rev16
--- dataset_info: - config_name: full features: - name: audio dtype: audio - name: file_number dtype: string - name: show_title dtype: string - name: episode_title dtype: string - name: itunes_id dtype: string - name: transcription dtype: string splits: - name: test num_bytes: 1509910660.0 num_examples: 30 download_size: 1445493754 dataset_size: 1509910660.0 - config_name: whisper_subset features: - name: audio dtype: audio - name: file_number dtype: string - name: show_title dtype: string - name: episode_title dtype: string - name: itunes_id dtype: string - name: transcription dtype: string splits: - name: test num_bytes: 921693242.0 num_examples: 16 download_size: 881542397 dataset_size: 921693242.0 configs: - config_name: full data_files: - split: test path: full/test-* - config_name: whisper_subset data_files: - split: test path: whisper_subset/test-* --- # Dataset Card for "rev16" Configs: * `full`: the entire 30 podcast files * `whisper_subset`: the subset of 16 podcast files used in the Whisper paper for long-form evaluation. The remaining 14 files have mis-matches between the audio and labels, and are thus filtered from the test set.
MohammadJamalaldeen/google_fleurs_ar
--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 2021551248 num_examples: 2104 - name: test num_bytes: 411235560 num_examples: 428 download_size: 901727231 dataset_size: 2432786808 --- # Dataset Card for "google_fleurs_ar" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
huggingartists/the-69-eyes
--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/the-69-eyes" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 0.162381 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/9e0451fa9d3f8cf38aa11994dbd934a8.600x600x1.jpg&#39;)"> </div> </div> <a href="https://huggingface.co/huggingartists/the-69-eyes"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">The 69 Eyes</div> <a href="https://genius.com/artists/the-69-eyes"> <div style="text-align: center; font-size: 14px;">@the-69-eyes</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/the-69-eyes). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/the-69-eyes") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |168| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/the-69-eyes") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
CyberHarem/perfumer_arknights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of perfumer/パフューマー/调香师 (Arknights) This is the dataset of perfumer/パフューマー/调香师 (Arknights), containing 229 images and their tags. The core tags of this character are `animal_ears, brown_hair, fox_ears, ponytail, bow, hair_bow, brown_eyes, long_hair, blue_bow, fox_girl, breasts, tail, striped_bow`, 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 | 229 | 341.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/perfumer_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 229 | 295.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/perfumer_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 564 | 575.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/perfumer_arknights/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/perfumer_arknights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 7 | ![](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, bare_shoulders, long_sleeves, off_shoulder, open_jacket, solo, white_dress, looking_at_viewer, blue_jacket, collarbone, holding_staff, smile, fur_trim, cowboy_shot, white_flower | | 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, bare_shoulders, holding_staff, looking_at_viewer, off_shoulder, solo, fox_tail, long_sleeves, open_jacket, white_dress, blue_jacket, full_body, high_heels, simple_background, white_background, black_footwear, collarbone, frilled_dress, smile, animal, black_jacket, white_flower, fur_trim | | 2 | 7 | ![](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, black_pantyhose, fox_tail, long_sleeves, white_shirt, belt, id_card, solo, closed_mouth, collared_shirt, holding, looking_at_viewer, official_alternate_costume, smile, black_footwear, full_body, shoes, simple_background, vial, white_background, white_jacket, black_skirt, cowboy_shot, hand_up | | 3 | 6 | ![](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, dress, hairband, long_sleeves, official_alternate_costume, solo, closed_mouth, hair_between_eyes, looking_at_viewer, simple_background, smile, yellow_bow, fox, holding_book, open_book, sitting, upper_body, white_background | | 4 | 5 | ![](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, completely_nude, fox_tail, cum_in_pussy, medium_breasts, nipples, sweat, 1boy, anus, hetero, looking_at_viewer, lying, mosaic_censoring, open_mouth, ass, collarbone, cum_overflow, hair_between_eyes, heart-shaped_pupils, looking_back, on_bed, penis, sex_from_behind, solo_focus, spread_legs, vaginal | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | long_sleeves | off_shoulder | open_jacket | solo | white_dress | looking_at_viewer | blue_jacket | collarbone | holding_staff | smile | fur_trim | cowboy_shot | white_flower | fox_tail | full_body | high_heels | simple_background | white_background | black_footwear | frilled_dress | animal | black_jacket | black_pantyhose | white_shirt | belt | id_card | closed_mouth | collared_shirt | holding | official_alternate_costume | shoes | vial | white_jacket | black_skirt | hand_up | dress | hairband | hair_between_eyes | yellow_bow | fox | holding_book | open_book | sitting | upper_body | blush | completely_nude | cum_in_pussy | medium_breasts | nipples | sweat | 1boy | anus | hetero | lying | mosaic_censoring | open_mouth | ass | cum_overflow | heart-shaped_pupils | looking_back | on_bed | penis | sex_from_behind | solo_focus | spread_legs | vaginal | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:---------------|:--------------|:-------|:--------------|:--------------------|:--------------|:-------------|:----------------|:--------|:-----------|:--------------|:---------------|:-----------|:------------|:-------------|:--------------------|:-------------------|:-----------------|:----------------|:---------|:---------------|:------------------|:--------------|:-------|:----------|:---------------|:-----------------|:----------|:-----------------------------|:--------|:-------|:---------------|:--------------|:----------|:--------|:-----------|:--------------------|:-------------|:------|:---------------|:------------|:----------|:-------------|:--------|:------------------|:---------------|:-----------------|:----------|:--------|:-------|:-------|:---------|:--------|:-------------------|:-------------|:------|:---------------|:----------------------|:---------------|:---------|:--------|:------------------|:-------------|:--------------|:----------| | 0 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | | | X | | X | | | | X | | X | | X | X | | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | | | | | X | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
datadreamer-dev/abstracts_and_tweets
--- dataset_info: features: - name: abstracts dtype: string - name: prompts dtype: string - name: tweets dtype: string splits: - name: train num_bytes: 3127163 num_examples: 900 - name: validation num_bytes: 343839 num_examples: 100 download_size: 1765300 dataset_size: 3471002 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* library_name: datadreamer size_categories: - 1K<n<10K tags: - datadreamer - datadreamer-0.1.0 - synthetic - gpt-4 - gpt-4 --- # Dataset Card This a synthetic dataset of arXiv-style research paper abstracts and tweets summarizing them used as a demonstration of the [DataDreamer 🤖💤 library](https://datadreamer.dev/docs/latest/). It was used to train an ["Abstract to Tweet" model](https://huggingface.co/datadreamer-dev/abstracts_to_tweet_model). --- This dataset was produced with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card can be found [here](datadreamer.json).
emmas96/Lenselink
--- license: gpl-3.0 ---
automated-research-group/llama2_7b-arc_hard-results_playing
--- dataset_info: config_name: '{''do_sample''=False, ''beams''=1}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 11410 num_examples: 299 download_size: 9803 dataset_size: 11410 configs: - config_name: '{''do_sample''=False, ''beams''=1}' data_files: - split: train path: '{''do_sample''=False, ''beams''=1}/train-*' --- # Dataset Card for "llama2_7b-arc_hard-results_playing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
harishvs/imdb_review_prompt_small
--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos splits: - name: train num_bytes: 145038 num_examples: 391 - name: test num_bytes: 152454 num_examples: 429 download_size: 147356 dataset_size: 297492 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
cowlag/sd-webui-config
--- license: unknown ---
ilhamxx/my_data_receipt
--- license: unknown ---
afasafen/mydataset
--- license: afl-3.0 ---
sourcegraph/fine-tune-unit-test-call-exp-context-dataset-java
--- dataset_info: features: - name: repo_url dtype: string - name: language dtype: string - name: source_file_path dtype: string - name: test_file_path dtype: string - name: source_fn_block dtype: string - name: source_fn_name dtype: string - name: test_fn_block dtype: string - name: test_fn_name dtype: string - name: source_fn_call_exps sequence: string - name: test_fn_call_exps sequence: string - name: test_file_additional_context struct: - name: class_fields dtype: string - name: class_name dtype: string - name: source_file_additional_context struct: - name: class_fields dtype: string - name: class_name dtype: string - name: method_signatures dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: response list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 992106219 num_examples: 99977 - name: validation num_bytes: 130304452 num_examples: 12807 - name: test num_bytes: 140926073 num_examples: 13825 download_size: 234465582 dataset_size: 1263336744 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
crumb/askmistral-pile-011-filtered
--- dataset_info: features: - name: text dtype: string - name: pos dtype: float64 splits: - name: train num_bytes: 5200925105.26056 num_examples: 669990 download_size: 3017098561 dataset_size: 5200925105.26056 configs: - config_name: default data_files: - split: train path: data/train-* ---
BangumiBase/deatte5byoudebattle
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Deatte 5-byou De Battle This is the image base of bangumi Deatte 5-byou de Battle, we detected 30 characters, 2195 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 | 143 | [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 | 94 | [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 | 34 | [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 | 24 | [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 | 127 | [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 | 29 | [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 | 22 | [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 | 68 | [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 | 81 | [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 | 42 | [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 | 12 | [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 | 61 | [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 | 127 | [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 | 67 | [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 | 10 | [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 | 25 | [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 | 12 | [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 | 105 | [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 | 52 | [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 | 5 | [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) | N/A | N/A | N/A | | 21 | 74 | [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 | 27 | [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 | 20 | [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 | 7 | [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) | N/A | | 25 | 297 | [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 | 7 | [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) | N/A | | 27 | 17 | [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 | 27 | [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) | | noise | 545 | [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) |
XYLF/autotrain-data-flan-t5-tuning
--- task_categories: - translation --- # AutoTrain Dataset for project: flan-t5-tuning ## Dataset Description This dataset has been automatically processed by AutoTrain for project flan-t5-tuning. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "target": "G(!( oGupGnpHlFihSN ))", "source": "it never happens that oGupGnpHlFihSN", "feat_Unnamed: 2": null }, { "target": "G(!( uJwMVmQcOjk & NFbgbwYf & uwbnvOQXgDVD ))", "source": "at no time uJwMVmQcOjk and, at the same time, NFbgbwYf and uwbnvOQXgDVD", "feat_Unnamed: 2": null } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "target": "Value(dtype='string', id=None)", "source": "Value(dtype='string', id=None)", "feat_Unnamed: 2": "Value(dtype='float64', 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 | 2399 | | valid | 600 |
MLRS/masri_synthetic
--- annotations_creators: - machine-generated language: - mt language_creators: - machine-generated license: cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: "MASRI-SYNTHETIC: Synthetized Speech with Transcriptions in Maltese." size_categories: - 10K<n<100K source_datasets: - original tags: - masri - maltese - masri-project - malta - synthetic speech - tts task_categories: - automatic-speech-recognition task_ids: [] --- # Dataset Card for masri_synthetic ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [MASRI Project](https://www.um.edu.mt/projects/masri/) - **Repository:** [MASRI Data Repo](https://github.com/UMSpeech/) - **Repository:** [LDC](https://catalog.ldc.upenn.edu/LDC2022S08) - **Paper:** [Data Augmentation for Speech Recognition in Maltese: A Low-Resource Perspective](https://www.um.edu.mt/library/oar/bitstream/123456789/92466/1/Data_Augmentation_for_Speech_Recognition_in_Maltese_A_Low_Resource_Perspective%282021%29.pdf) - **Paper:** [Analysis of Data Augmentation Methods for Low-Resource Maltese ASR](https://arxiv.org/pdf/2111.07793.pdf) ### Dataset Summary The MASRI-SYNTHETIC is a corpus made out of synthesized speech in Maltese. The text-to-speech (TTS) system utilized to produce the utterances was developed by the Research & Development Department of Crimsonwing p.l.c. The sentences used to create the corpus were extracted from the [MLRS Corpus](https://mlrs.research.um.edu.mt/index.php?page=corpora), which is a corpus of written or transcribed Maltese divided into different genres, including: culture, news, academic, religion, sports, etc. [MASRI](https://www.um.edu.mt/projects/masri/) stands for "Maltese Automatic Speech Recognition I". [MASRI](https://www.um.edu.mt/projects/masri/) is a project at the [University of Malta](https://www.um.edu.mt/), funded by the University of Malta Research Fund Award Scheme. ### Example Usage The MASRI-SYNTHETIC contains the train split only: ```python from datasets import load_dataset masri_synthetic = load_dataset("MLRS/masri_synthetic") ``` It is also valid to do: ```python from datasets import load_dataset masri_synthetic = load_dataset("MLRS/masri_synthetic",split="train") ``` ### Supported Tasks automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). ### Languages The language of the corpus is Maltese. ## Dataset Structure ### Data Instances ```python { 'audio_id': 'MSRSY_F_0042_RN01PP10_0143', 'audio': { 'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/17d8c60020489a5a43ba0cf322ed7c121375915c671b57fbdb03950befbd1a9c/female/F_0042_RN01PP10/MSRSY_F_0042_RN01PP10_0143.flac', 'array': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), 'sampling_rate': 16000 }, 'speaker_id': 'F_0042', 'gender': 'female', 'duration': 9.0, 'speech_rate': '-01', 'pitch': '+10', 'normalized_text': "il-poplu b' pakkett ta' negozjati f' id-direttur ġenerali tal-uffiċċju tal-pubblikazzjonijiet uffiċjali għall-komunitajiet ewropej" } ``` ### Data Fields * `audio_id` (string) - id of audio segment * `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally). * `speaker_id` (string) - id of the synthetic voice * `gender` (string) - gender of synthetic voice (male or female) * `duration` (float32) - duration of the audio file in seconds. * `speech_rate` (string) - speed rate that goes from -2 to +2. * `pitch` (string) - the pitch goes from -10 to +10. * `normalized_text` (string) - normalized audio segment transcription ### Data Splits The corpus counts just with the train split which has a total of 52500 speech files from 105 male and 105 female voices with a total duration of 99 hours and 18 minutes. ## Dataset Creation ### Curation Rationale The MASRI-SYNTHETIC CORPUS (MSYC) has the following characteristics: * The MSYC has an exact duration of 99 hours and 18 minutes. It has 52500 audio files. * The MSYC has recordings from 210 different voices: 105 men and 105 female voices. * Voices were produced when varying between 21 values of pitch (-10 to +10) and 5 values of speech rate (-2 to 2). * Data in MSYC is classified by voice. It means, all the utterances belonging to one single voice are stored in one single directory. * Data is also classified according to the gender (male/female) of the voice. * Each voice has assigned 250 utterances of 13 words each. * Every audio file in the MSYC has a duration between 2 and 10 seconds approximately. * Audio files in the MSYC are distributed in a 16khz@16bit mono format. * Transcriptions in MSYC are in lowercase. No punctuation marks are permitted except for dashes (-) and apostrophes (') due to their importance in Maltese orthography. * Every audio file has an ID that is compatible with ASR engines such as Kaldi and CMU-Sphinx. ### Source Data #### Initial Data Collection and Normalization The MASRI-SYNTHETIC CORPUS was possible thanks to the text-to-speech (TTS) system developed by the Research & Development Department of Crimsonwing p.l.c. The sentences used to create the corpus were extracted from the [MLRS Corpus](https://mlrs.research.um.edu.mt/index.php?page=corpora). ### Annotations #### Annotation process Text sentences from the platform [MLRS Corpus](https://mlrs.research.um.edu.mt/index.php?page=corpora) were selected to create synthetic utterances with them. The MASRI-SYNTHETIC is comprised of synthetic utterances only. #### Who are the annotators? The authors selected the sentences to be synthesized. ### Personal and Sensitive Information The corpus is comprised of synthetic speech utterances from a TTS system. No personal or sensitive information is shared. ## Considerations for Using the Data ### Social Impact of Dataset The MASRI-SYNTHETIC CORPUS is the only Maltese corpus at the moment, that counts with synthetic speech and it is publicly available under a CC-BY-NC-SA-4.0 license. ### Discussion of Biases * Sentences from [MLRS]((https://mlrs.research.um.edu.mt/index.php?page=corpora)) are put in a single plain text file. The text includes punctuation marks. * To facilitate the text processing, sentences are split to fit into lines with 30 words only. * Punctuation marks and sentences including not UTF-8 characters are removed. * Sentences with foreign words and proper names were removed. * As the letters "c" and "y" do not really belong to the Maltese alphabet, sentences including words with any of those letters were removed. This is done to ensure that only Maltese words will be included in each sentence. * Using Python, the resulting sentences are now put into a simple list; so, each element is a word. * Each word of the list is now taken one by one to produce text lines of exactly 13 words. This process only generated 27714 sentences of the 52500 that constitute the whole corpus. * To produce the remaining sentences, the words of the list were shuffled and the process in the previous point were repeated until we got the 52500 sentences needed by the corpus. * At the end, the produced sentences were converted into utterances using the TTS system. ### Other Known Limitations The MASRI team does not guarantee the accuracy of this corpus, nor its suitability for any specific purpose. In fact, we expect a number of errors, omissions and inconsistencies to remain in the corpus. ### Dataset Curators The speech sentences were selected and synthesized by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena) at the [University of Malta](https://www.um.edu.mt/) in the Msida Campus during June, 2020. ### Licensing Information [CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) ### Citation Information ``` @misc{carlosmenamasrisynthetic2020, title={MASRI-SYNTHETIC: Synthetized Speech with Transcriptions in Maltese.}, author={Hernandez Mena, Carlos Daniel and Gatt, Albert and DeMarco, Andrea and Borg, Claudia and van der Plas, Lonneke}, journal={MASRI Project, Malta}, year={2020}, url={https://huggingface.co/datasets/MLRS/masri_synthetic}, } ``` The MASRI-SYNTHETIC was also published at [LDC](https://catalog.ldc.upenn.edu/LDC2022S08) in 2022. ### Contributions The authors would like to thank to KPMG Microsoft Business Solutions (formerly CrimsonWing) for providing the TTS system used in our experiments. For more information about the CrimsonWing TTS system see [this presentation](https://pdfs.semanticscholar.org/5e5a/25e34b3c351ba0e58211a5192535e9ddea06.pdf). We also want to thant to the University of Malta Research Fund Award Scheme for making this project possible.
SiberiaSoft/SiberianDataset
--- license: mit task_categories: - text-generation - text2text-generation - conversational language: - ru size_categories: - 100K<n<1M --- ### SiberiaSoft/SiberianDataset Датасет инструкций, диалогов, QA ## Процентное содержание задач: | Задача | Процентное содержание | |:----------------------------------------------------------------------------:|:---------------------:| | Чит-чат с контекстом | 40.092% | | Чит-чат без контекста (синтетика) | 15.391% | | QA с короткими ответами | 14.045% | | Инструкции с its5Q/yandex-q | 6.292% | | Инструкции с Den4ikAI/russian_instructions_2 | 4.568% | | Инструкции с lksy/ru_instruct_gpt4 (жестко очищенные) | 4.492% | | Инструкции с IlyaGusev/ru_turbo_alpaca_evol_instruct (очень жестко очищенные)| 4.442% | | QA с длинными, развернутыми ответами | 4.441% | | QA с использованием Wikipedia | 3.617% | | Ответы на вопросы по тексту Den4ikAI/ru_sberquad_long_answers | 2.448% | | Решение проблем | 0.14% | | QA Объясни ребенку | 0.034% | ### Citation ``` @MISC{SiberianDataset, author = {Denis Petrov, Ivan Ramovich}, title = {Russian dataset for Instruct/Chat models}, url = {https://huggingface.co/datasets/SiberiaSoft/SiberianDataset}, year = 2023 } ```
sinandraide/hotpot_qa_spread
--- task_categories: - question-answering language: - en size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name This dataset is a spread version of the HotpotQA dataset. This version allows it to be compatible with Langchain's HuggingfaceLoader. This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## 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 The source data set is from https://huggingface.co/datasets/hotpot_qa. The original authors are Yang et al. (2018) https://arxiv.org/abs/1809.09600. #### 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]
open-llm-leaderboard/details_athirdpath__Iambe-20b-DARE-v2
--- pretty_name: Evaluation run of athirdpath/Iambe-20b-DARE-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [athirdpath/Iambe-20b-DARE-v2](https://huggingface.co/athirdpath/Iambe-20b-DARE-v2)\ \ 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_athirdpath__Iambe-20b-DARE-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-08T02:48:17.586217](https://huggingface.co/datasets/open-llm-leaderboard/details_athirdpath__Iambe-20b-DARE-v2/blob/main/results_2023-12-08T02-48-17.586217.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\": 0.6035804809886537,\n\ \ \"acc_stderr\": 0.03294194113186395,\n \"acc_norm\": 0.608982387572558,\n\ \ \"acc_norm_stderr\": 0.0336160701060513,\n \"mc1\": 0.390452876376989,\n\ \ \"mc1_stderr\": 0.01707823074343145,\n \"mc2\": 0.5385363923413744,\n\ \ \"mc2_stderr\": 0.01567101081137168\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6023890784982935,\n \"acc_stderr\": 0.014301752223279538,\n\ \ \"acc_norm\": 0.6279863481228669,\n \"acc_norm_stderr\": 0.014124597881844456\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6562437761402111,\n\ \ \"acc_stderr\": 0.004739902411944536,\n \"acc_norm\": 0.8453495319657439,\n\ \ \"acc_norm_stderr\": 0.0036083220651418873\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5037037037037037,\n\ \ \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.5037037037037037,\n\ \ \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n\ \ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\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\": 0.6339622641509434,\n \"acc_stderr\": 0.029647813539365245,\n\ \ \"acc_norm\": 0.6339622641509434,\n \"acc_norm_stderr\": 0.029647813539365245\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n\ \ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\ \ \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\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\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5317919075144508,\n\ \ \"acc_stderr\": 0.038047497443647646,\n \"acc_norm\": 0.5317919075144508,\n\ \ \"acc_norm_stderr\": 0.038047497443647646\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077615,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077615\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.49361702127659574,\n \"acc_stderr\": 0.032683358999363366,\n\ \ \"acc_norm\": 0.49361702127659574,\n \"acc_norm_stderr\": 0.032683358999363366\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\ \ \"acc_stderr\": 0.04339138322579861,\n \"acc_norm\": 0.30701754385964913,\n\ \ \"acc_norm_stderr\": 0.04339138322579861\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3412698412698413,\n \"acc_stderr\": 0.024419234966819067,\n \"\ acc_norm\": 0.3412698412698413,\n \"acc_norm_stderr\": 0.024419234966819067\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7161290322580646,\n\ \ \"acc_stderr\": 0.02564938106302926,\n \"acc_norm\": 0.7161290322580646,\n\ \ \"acc_norm_stderr\": 0.02564938106302926\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\ : 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.035014387062967806,\n\ \ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.035014387062967806\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7424242424242424,\n \"acc_stderr\": 0.031156269519646836,\n \"\ acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.031156269519646836\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.02541634309630644,\n\ \ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.02541634309630644\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.02469721693087894,\n \ \ \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.02469721693087894\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473072,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473072\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6302521008403361,\n \"acc_stderr\": 0.031357095996135904,\n\ \ \"acc_norm\": 0.6302521008403361,\n \"acc_norm_stderr\": 0.031357095996135904\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3973509933774834,\n \"acc_stderr\": 0.039955240076816806,\n \"\ acc_norm\": 0.3973509933774834,\n \"acc_norm_stderr\": 0.039955240076816806\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7761467889908257,\n \"acc_stderr\": 0.017871217767790222,\n \"\ acc_norm\": 0.7761467889908257,\n \"acc_norm_stderr\": 0.017871217767790222\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.803921568627451,\n \"acc_stderr\": 0.027865942286639325,\n \"\ acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639325\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7932489451476793,\n \"acc_stderr\": 0.026361651668389087,\n \ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.026361651668389087\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.040103589424622034,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.040103589424622034\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\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.754601226993865,\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\": 0.33035714285714285,\n\ \ \"acc_stderr\": 0.04464285714285714,\n \"acc_norm\": 0.33035714285714285,\n\ \ \"acc_norm_stderr\": 0.04464285714285714\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.04453254836326468,\n\ \ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.04453254836326468\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\ \ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\ \ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7905491698595147,\n\ \ \"acc_stderr\": 0.014551310568143705,\n \"acc_norm\": 0.7905491698595147,\n\ \ \"acc_norm_stderr\": 0.014551310568143705\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.684971098265896,\n \"acc_stderr\": 0.025009313790069716,\n\ \ \"acc_norm\": 0.684971098265896,\n \"acc_norm_stderr\": 0.025009313790069716\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.48268156424581005,\n\ \ \"acc_stderr\": 0.016712467441702523,\n \"acc_norm\": 0.48268156424581005,\n\ \ \"acc_norm_stderr\": 0.016712467441702523\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6633986928104575,\n \"acc_stderr\": 0.027057974624494382,\n\ \ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.027057974624494382\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\ \ \"acc_stderr\": 0.026311858071854155,\n \"acc_norm\": 0.6881028938906752,\n\ \ \"acc_norm_stderr\": 0.026311858071854155\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.02532988817190092,\n\ \ \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.02532988817190092\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47131681877444587,\n\ \ \"acc_stderr\": 0.012749206007657476,\n \"acc_norm\": 0.47131681877444587,\n\ \ \"acc_norm_stderr\": 0.012749206007657476\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5808823529411765,\n \"acc_stderr\": 0.029972807170464626,\n\ \ \"acc_norm\": 0.5808823529411765,\n \"acc_norm_stderr\": 0.029972807170464626\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6160130718954249,\n \"acc_stderr\": 0.019675808135281515,\n \ \ \"acc_norm\": 0.6160130718954249,\n \"acc_norm_stderr\": 0.019675808135281515\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.0469237132203465,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.0469237132203465\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6816326530612244,\n \"acc_stderr\": 0.029822533793982062,\n\ \ \"acc_norm\": 0.6816326530612244,\n \"acc_norm_stderr\": 0.029822533793982062\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786848,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786848\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n\ \ \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n\ \ \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.032180937956023566,\n\ \ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.032180937956023566\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.390452876376989,\n\ \ \"mc1_stderr\": 0.01707823074343145,\n \"mc2\": 0.5385363923413744,\n\ \ \"mc2_stderr\": 0.01567101081137168\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838229\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.332827899924185,\n \ \ \"acc_stderr\": 0.012979892496598268\n }\n}\n```" repo_url: https://huggingface.co/athirdpath/Iambe-20b-DARE-v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|arc:challenge|25_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-08T02-48-17.586217.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|gsm8k|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hellaswag|10_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-08T02-48-17.586217.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-management|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T02-48-17.586217.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|truthfulqa:mc|0_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-08T02-48-17.586217.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_08T02_48_17.586217 path: - '**/details_harness|winogrande|5_2023-12-08T02-48-17.586217.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-08T02-48-17.586217.parquet' - config_name: results data_files: - split: 2023_12_08T02_48_17.586217 path: - results_2023-12-08T02-48-17.586217.parquet - split: latest path: - results_2023-12-08T02-48-17.586217.parquet --- # Dataset Card for Evaluation run of athirdpath/Iambe-20b-DARE-v2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/athirdpath/Iambe-20b-DARE-v2 - **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 [athirdpath/Iambe-20b-DARE-v2](https://huggingface.co/athirdpath/Iambe-20b-DARE-v2) 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_athirdpath__Iambe-20b-DARE-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-08T02:48:17.586217](https://huggingface.co/datasets/open-llm-leaderboard/details_athirdpath__Iambe-20b-DARE-v2/blob/main/results_2023-12-08T02-48-17.586217.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": 0.6035804809886537, "acc_stderr": 0.03294194113186395, "acc_norm": 0.608982387572558, "acc_norm_stderr": 0.0336160701060513, "mc1": 0.390452876376989, "mc1_stderr": 0.01707823074343145, "mc2": 0.5385363923413744, "mc2_stderr": 0.01567101081137168 }, "harness|arc:challenge|25": { "acc": 0.6023890784982935, "acc_stderr": 0.014301752223279538, "acc_norm": 0.6279863481228669, "acc_norm_stderr": 0.014124597881844456 }, "harness|hellaswag|10": { "acc": 0.6562437761402111, "acc_stderr": 0.004739902411944536, "acc_norm": 0.8453495319657439, "acc_norm_stderr": 0.0036083220651418873 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5037037037037037, "acc_stderr": 0.04319223625811331, "acc_norm": 0.5037037037037037, "acc_norm_stderr": 0.04319223625811331 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.618421052631579, "acc_stderr": 0.03953173377749194, "acc_norm": 0.618421052631579, "acc_norm_stderr": 0.03953173377749194 }, "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": 0.6339622641509434, "acc_stderr": 0.029647813539365245, "acc_norm": 0.6339622641509434, "acc_norm_stderr": 0.029647813539365245 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7013888888888888, "acc_stderr": 0.03827052357950756, "acc_norm": 0.7013888888888888, "acc_norm_stderr": 0.03827052357950756 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5317919075144508, "acc_stderr": 0.038047497443647646, "acc_norm": 0.5317919075144508, "acc_norm_stderr": 0.038047497443647646 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.045766654032077615, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.045766654032077615 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.49361702127659574, "acc_stderr": 0.032683358999363366, "acc_norm": 0.49361702127659574, "acc_norm_stderr": 0.032683358999363366 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.30701754385964913, "acc_stderr": 0.04339138322579861, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.04339138322579861 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3412698412698413, "acc_stderr": 0.024419234966819067, "acc_norm": 0.3412698412698413, "acc_norm_stderr": 0.024419234966819067 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7161290322580646, "acc_stderr": 0.02564938106302926, "acc_norm": 0.7161290322580646, "acc_norm_stderr": 0.02564938106302926 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4876847290640394, "acc_stderr": 0.035169204442208966, "acc_norm": 0.4876847290640394, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7212121212121212, "acc_stderr": 0.035014387062967806, "acc_norm": 0.7212121212121212, "acc_norm_stderr": 0.035014387062967806 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7424242424242424, "acc_stderr": 0.031156269519646836, "acc_norm": 0.7424242424242424, "acc_norm_stderr": 0.031156269519646836 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8549222797927462, "acc_stderr": 0.02541634309630644, "acc_norm": 0.8549222797927462, "acc_norm_stderr": 0.02541634309630644 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6128205128205129, "acc_stderr": 0.02469721693087894, "acc_norm": 0.6128205128205129, "acc_norm_stderr": 0.02469721693087894 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473072, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473072 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6302521008403361, "acc_stderr": 0.031357095996135904, "acc_norm": 0.6302521008403361, "acc_norm_stderr": 0.031357095996135904 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3973509933774834, "acc_stderr": 0.039955240076816806, "acc_norm": 0.3973509933774834, "acc_norm_stderr": 0.039955240076816806 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7761467889908257, "acc_stderr": 0.017871217767790222, "acc_norm": 0.7761467889908257, "acc_norm_stderr": 0.017871217767790222 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 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0.03800754475228732 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "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": 0.33035714285714285, "acc_stderr": 0.04464285714285714, "acc_norm": 0.33035714285714285, "acc_norm_stderr": 0.04464285714285714 }, "harness|hendrycksTest-management|5": { "acc": 0.7184466019417476, "acc_stderr": 0.04453254836326468, "acc_norm": 0.7184466019417476, "acc_norm_stderr": 0.04453254836326468 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.61, "acc_stderr": 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"harness|hendrycksTest-prehistory|5": { "acc": 0.7067901234567902, "acc_stderr": 0.02532988817190092, "acc_norm": 0.7067901234567902, "acc_norm_stderr": 0.02532988817190092 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47131681877444587, "acc_stderr": 0.012749206007657476, "acc_norm": 0.47131681877444587, "acc_norm_stderr": 0.012749206007657476 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5808823529411765, "acc_stderr": 0.029972807170464626, "acc_norm": 0.5808823529411765, "acc_norm_stderr": 0.029972807170464626 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6160130718954249, "acc_stderr": 0.019675808135281515, "acc_norm": 0.6160130718954249, "acc_norm_stderr": 0.019675808135281515 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6, "acc_stderr": 0.0469237132203465, "acc_norm": 0.6, "acc_norm_stderr": 0.0469237132203465 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6816326530612244, "acc_stderr": 0.029822533793982062, "acc_norm": 0.6816326530612244, "acc_norm_stderr": 0.029822533793982062 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786848, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786848 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness|hendrycksTest-virology|5": { "acc": 0.4819277108433735, "acc_stderr": 0.038899512528272166, "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7719298245614035, "acc_stderr": 0.032180937956023566, "acc_norm": 0.7719298245614035, "acc_norm_stderr": 0.032180937956023566 }, "harness|truthfulqa:mc|0": { "mc1": 0.390452876376989, "mc1_stderr": 0.01707823074343145, "mc2": 0.5385363923413744, "mc2_stderr": 0.01567101081137168 }, "harness|winogrande|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838229 }, "harness|gsm8k|5": { "acc": 0.332827899924185, "acc_stderr": 0.012979892496598268 } } ``` ### 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]
CyberHarem/selfira_granbluefantasy
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of selfira (Granblue Fantasy) This is the dataset of selfira (Granblue Fantasy), containing 11 images and their tags. The core tags of this character are `animal_ears, red_hair, long_hair, bangs, breasts, ponytail, mole, medium_breasts, brown_eyes, mole_under_eye, brown_hair`, 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 | 11 | 9.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selfira_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 11 | 6.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selfira_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 25 | 13.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selfira_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 11 | 8.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selfira_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 25 | 15.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selfira_granbluefantasy/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/selfira_granbluefantasy', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, erune, solo, looking_at_viewer, red_dress, bare_shoulders, simple_background, detached_sleeves, bare_back, cape, from_behind, looking_back, ass, backless_dress, blush, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | erune | solo | looking_at_viewer | red_dress | bare_shoulders | simple_background | detached_sleeves | bare_back | cape | from_behind | looking_back | ass | backless_dress | blush | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:------------|:-----------------|:--------------------|:-------------------|:------------|:-------|:--------------|:---------------|:------|:-----------------|:--------|:-------------------| | 0 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
open-llm-leaderboard/details_jan-hq__LlamaCorn-1.1B-Chat
--- pretty_name: Evaluation run of jan-hq/LlamaCorn-1.1B-Chat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jan-hq/LlamaCorn-1.1B-Chat](https://huggingface.co/jan-hq/LlamaCorn-1.1B-Chat)\ \ 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_jan-hq__LlamaCorn-1.1B-Chat\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-12T10:29:25.854017](https://huggingface.co/datasets/open-llm-leaderboard/details_jan-hq__LlamaCorn-1.1B-Chat/blob/main/results_2024-03-12T10-29-25.854017.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\": 0.29373569196097277,\n\ \ \"acc_stderr\": 0.032243803435004895,\n \"acc_norm\": 0.2960484401036193,\n\ \ \"acc_norm_stderr\": 0.03310756115855655,\n \"mc1\": 0.23378212974296206,\n\ \ \"mc1_stderr\": 0.014816195991931586,\n \"mc2\": 0.36855840909843307,\n\ \ \"mc2_stderr\": 0.013989365630749612\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.318259385665529,\n \"acc_stderr\": 0.013611993916971451,\n\ \ \"acc_norm\": 0.3378839590443686,\n \"acc_norm_stderr\": 0.013822047922283516\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4482174865564629,\n\ \ \"acc_stderr\": 0.0049629497842360445,\n \"acc_norm\": 0.5924118701453893,\n\ \ \"acc_norm_stderr\": 0.004903815885983271\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n\ \ \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n\ \ \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.24342105263157895,\n \"acc_stderr\": 0.034923496688842384,\n\ \ \"acc_norm\": 0.24342105263157895,\n \"acc_norm_stderr\": 0.034923496688842384\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.37,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2943396226415094,\n \"acc_stderr\": 0.028049186315695245,\n\ \ \"acc_norm\": 0.2943396226415094,\n \"acc_norm_stderr\": 0.028049186315695245\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\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness|hendrycksTest-college_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-college_mathematics|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.23699421965317918,\n\ \ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n\ \ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.042801058373643966,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.042801058373643966\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n\ \ \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.32340425531914896,\n \"acc_stderr\": 0.030579442773610337,\n\ \ \"acc_norm\": 0.32340425531914896,\n \"acc_norm_stderr\": 0.030579442773610337\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n\ \ \"acc_stderr\": 0.04185774424022056,\n \"acc_norm\": 0.2719298245614035,\n\ \ \"acc_norm_stderr\": 0.04185774424022056\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.296551724137931,\n \"acc_stderr\": 0.03806142687309994,\n\ \ \"acc_norm\": 0.296551724137931,\n \"acc_norm_stderr\": 0.03806142687309994\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2830687830687831,\n \"acc_stderr\": 0.023201392938194978,\n \"\ acc_norm\": 0.2830687830687831,\n \"acc_norm_stderr\": 0.023201392938194978\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n\ \ \"acc_stderr\": 0.03852273364924316,\n \"acc_norm\": 0.24603174603174602,\n\ \ \"acc_norm_stderr\": 0.03852273364924316\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24193548387096775,\n\ \ \"acc_stderr\": 0.024362599693031083,\n \"acc_norm\": 0.24193548387096775,\n\ \ \"acc_norm_stderr\": 0.024362599693031083\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\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\"\ : 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.3515151515151515,\n \"acc_stderr\": 0.0372820699868265,\n\ \ \"acc_norm\": 0.3515151515151515,\n \"acc_norm_stderr\": 0.0372820699868265\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2474747474747475,\n \"acc_stderr\": 0.03074630074212451,\n \"\ acc_norm\": 0.2474747474747475,\n \"acc_norm_stderr\": 0.03074630074212451\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.27461139896373055,\n \"acc_stderr\": 0.03221024508041153,\n\ \ \"acc_norm\": 0.27461139896373055,\n \"acc_norm_stderr\": 0.03221024508041153\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.022421273612923714,\n\ \ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.022421273612923714\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.23703703703703705,\n \"acc_stderr\": 0.025928876132766107,\n \ \ \"acc_norm\": 0.23703703703703705,\n \"acc_norm_stderr\": 0.025928876132766107\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136094,\n\ \ \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136094\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.24503311258278146,\n \"acc_stderr\": 0.03511807571804724,\n \"\ acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.03511807571804724\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.24770642201834864,\n \"acc_stderr\": 0.018508143602547822,\n \"\ acc_norm\": 0.24770642201834864,\n \"acc_norm_stderr\": 0.018508143602547822\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.2824074074074074,\n \"acc_stderr\": 0.030701372111510934,\n \"\ acc_norm\": 0.2824074074074074,\n \"acc_norm_stderr\": 0.030701372111510934\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.29411764705882354,\n \"acc_stderr\": 0.031980016601150726,\n \"\ acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.031980016601150726\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.3881856540084388,\n \"acc_stderr\": 0.031722950043323296,\n \ \ \"acc_norm\": 0.3881856540084388,\n \"acc_norm_stderr\": 0.031722950043323296\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.40358744394618834,\n\ \ \"acc_stderr\": 0.032928028193303135,\n \"acc_norm\": 0.40358744394618834,\n\ \ \"acc_norm_stderr\": 0.032928028193303135\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.3282442748091603,\n \"acc_stderr\": 0.04118438565806299,\n\ \ \"acc_norm\": 0.3282442748091603,\n \"acc_norm_stderr\": 0.04118438565806299\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.36363636363636365,\n \"acc_stderr\": 0.043913262867240704,\n \"\ acc_norm\": 0.36363636363636365,\n \"acc_norm_stderr\": 0.043913262867240704\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.37037037037037035,\n\ \ \"acc_stderr\": 0.04668408033024931,\n \"acc_norm\": 0.37037037037037035,\n\ \ \"acc_norm_stderr\": 0.04668408033024931\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.2883435582822086,\n \"acc_stderr\": 0.035590395316173425,\n\ \ \"acc_norm\": 0.2883435582822086,\n \"acc_norm_stderr\": 0.035590395316173425\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3392857142857143,\n\ \ \"acc_stderr\": 0.04493949068613539,\n \"acc_norm\": 0.3392857142857143,\n\ \ \"acc_norm_stderr\": 0.04493949068613539\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.27184466019417475,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.27184466019417475,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3547008547008547,\n\ \ \"acc_stderr\": 0.03134250486245402,\n \"acc_norm\": 0.3547008547008547,\n\ \ \"acc_norm_stderr\": 0.03134250486245402\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.3269476372924649,\n\ \ \"acc_stderr\": 0.01677490818013146,\n \"acc_norm\": 0.3269476372924649,\n\ \ \"acc_norm_stderr\": 0.01677490818013146\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.30057803468208094,\n \"acc_stderr\": 0.024685316867257792,\n\ \ \"acc_norm\": 0.30057803468208094,\n \"acc_norm_stderr\": 0.024685316867257792\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24581005586592178,\n\ \ \"acc_stderr\": 0.014400296429225622,\n \"acc_norm\": 0.24581005586592178,\n\ \ \"acc_norm_stderr\": 0.014400296429225622\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.2679738562091503,\n \"acc_stderr\": 0.025360603796242567,\n\ \ \"acc_norm\": 0.2679738562091503,\n \"acc_norm_stderr\": 0.025360603796242567\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2990353697749196,\n\ \ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.2990353697749196,\n\ \ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2993827160493827,\n \"acc_stderr\": 0.025483115601195466,\n\ \ \"acc_norm\": 0.2993827160493827,\n \"acc_norm_stderr\": 0.025483115601195466\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880596,\n \ \ \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880596\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24511082138200782,\n\ \ \"acc_stderr\": 0.010986307870045503,\n \"acc_norm\": 0.24511082138200782,\n\ \ \"acc_norm_stderr\": 0.010986307870045503\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.21323529411764705,\n \"acc_stderr\": 0.02488097151229428,\n\ \ \"acc_norm\": 0.21323529411764705,\n \"acc_norm_stderr\": 0.02488097151229428\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2565359477124183,\n \"acc_stderr\": 0.017667841612378984,\n \ \ \"acc_norm\": 0.2565359477124183,\n \"acc_norm_stderr\": 0.017667841612378984\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.04389311454644286,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.04389311454644286\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.22857142857142856,\n \"acc_stderr\": 0.026882144922307748,\n\ \ \"acc_norm\": 0.22857142857142856,\n \"acc_norm_stderr\": 0.026882144922307748\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2736318407960199,\n\ \ \"acc_stderr\": 0.03152439186555401,\n \"acc_norm\": 0.2736318407960199,\n\ \ \"acc_norm_stderr\": 0.03152439186555401\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.30120481927710846,\n\ \ \"acc_stderr\": 0.0357160923005348,\n \"acc_norm\": 0.30120481927710846,\n\ \ \"acc_norm_stderr\": 0.0357160923005348\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.3216374269005848,\n \"acc_stderr\": 0.03582529442573122,\n\ \ \"acc_norm\": 0.3216374269005848,\n \"acc_norm_stderr\": 0.03582529442573122\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23378212974296206,\n\ \ \"mc1_stderr\": 0.014816195991931586,\n \"mc2\": 0.36855840909843307,\n\ \ \"mc2_stderr\": 0.013989365630749612\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6148382004735596,\n \"acc_stderr\": 0.013676821287521419\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/jan-hq/LlamaCorn-1.1B-Chat leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|arc:challenge|25_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-12T10-29-25.854017.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|gsm8k|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hellaswag|10_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-12T10-29-25.854017.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-management|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T10-29-25.854017.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|truthfulqa:mc|0_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-12T10-29-25.854017.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_12T10_29_25.854017 path: - '**/details_harness|winogrande|5_2024-03-12T10-29-25.854017.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-12T10-29-25.854017.parquet' - config_name: results data_files: - split: 2024_03_12T10_29_25.854017 path: - results_2024-03-12T10-29-25.854017.parquet - split: latest path: - results_2024-03-12T10-29-25.854017.parquet --- # Dataset Card for Evaluation run of jan-hq/LlamaCorn-1.1B-Chat <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jan-hq/LlamaCorn-1.1B-Chat](https://huggingface.co/jan-hq/LlamaCorn-1.1B-Chat) 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_jan-hq__LlamaCorn-1.1B-Chat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-12T10:29:25.854017](https://huggingface.co/datasets/open-llm-leaderboard/details_jan-hq__LlamaCorn-1.1B-Chat/blob/main/results_2024-03-12T10-29-25.854017.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": 0.29373569196097277, "acc_stderr": 0.032243803435004895, "acc_norm": 0.2960484401036193, "acc_norm_stderr": 0.03310756115855655, "mc1": 0.23378212974296206, "mc1_stderr": 0.014816195991931586, "mc2": 0.36855840909843307, "mc2_stderr": 0.013989365630749612 }, "harness|arc:challenge|25": { "acc": 0.318259385665529, "acc_stderr": 0.013611993916971451, "acc_norm": 0.3378839590443686, "acc_norm_stderr": 0.013822047922283516 }, "harness|hellaswag|10": { "acc": 0.4482174865564629, "acc_stderr": 0.0049629497842360445, "acc_norm": 0.5924118701453893, "acc_norm_stderr": 0.004903815885983271 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.23703703703703705, "acc_stderr": 0.03673731683969506, "acc_norm": 0.23703703703703705, "acc_norm_stderr": 0.03673731683969506 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.24342105263157895, "acc_stderr": 0.034923496688842384, "acc_norm": 0.24342105263157895, "acc_norm_stderr": 0.034923496688842384 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2943396226415094, "acc_stderr": 0.028049186315695245, "acc_norm": 0.2943396226415094, "acc_norm_stderr": 0.028049186315695245 }, "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": 0.26, "acc_stderr": 0.04408440022768078, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.23699421965317918, "acc_stderr": 0.03242414757483098, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483098 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.042801058373643966, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.042801058373643966 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.32340425531914896, "acc_stderr": 0.030579442773610337, "acc_norm": 0.32340425531914896, "acc_norm_stderr": 0.030579442773610337 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2719298245614035, "acc_stderr": 0.04185774424022056, "acc_norm": 0.2719298245614035, "acc_norm_stderr": 0.04185774424022056 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.296551724137931, "acc_stderr": 0.03806142687309994, "acc_norm": 0.296551724137931, "acc_norm_stderr": 0.03806142687309994 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2830687830687831, "acc_stderr": 0.023201392938194978, "acc_norm": 0.2830687830687831, "acc_norm_stderr": 0.023201392938194978 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.24603174603174602, "acc_stderr": 0.03852273364924316, "acc_norm": 0.24603174603174602, "acc_norm_stderr": 0.03852273364924316 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24193548387096775, "acc_stderr": 0.024362599693031083, "acc_norm": 0.24193548387096775, "acc_norm_stderr": 0.024362599693031083 }, "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": 0.28, "acc_stderr": 0.04512608598542129, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542129 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.3515151515151515, "acc_stderr": 0.0372820699868265, "acc_norm": 0.3515151515151515, "acc_norm_stderr": 0.0372820699868265 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2474747474747475, "acc_stderr": 0.03074630074212451, "acc_norm": 0.2474747474747475, "acc_norm_stderr": 0.03074630074212451 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.27461139896373055, "acc_stderr": 0.03221024508041153, "acc_norm": 0.27461139896373055, "acc_norm_stderr": 0.03221024508041153 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.26666666666666666, "acc_stderr": 0.022421273612923714, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.022421273612923714 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.23703703703703705, "acc_stderr": 0.025928876132766107, "acc_norm": 0.23703703703703705, "acc_norm_stderr": 0.025928876132766107 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.28991596638655465, "acc_stderr": 0.029472485833136094, "acc_norm": 0.28991596638655465, "acc_norm_stderr": 0.029472485833136094 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.24503311258278146, "acc_stderr": 0.03511807571804724, "acc_norm": 0.24503311258278146, "acc_norm_stderr": 0.03511807571804724 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.24770642201834864, "acc_stderr": 0.018508143602547822, "acc_norm": 0.24770642201834864, "acc_norm_stderr": 0.018508143602547822 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2824074074074074, "acc_stderr": 0.030701372111510934, "acc_norm": 0.2824074074074074, "acc_norm_stderr": 0.030701372111510934 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.29411764705882354, "acc_stderr": 0.031980016601150726, "acc_norm": 0.29411764705882354, "acc_norm_stderr": 0.031980016601150726 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.3881856540084388, "acc_stderr": 0.031722950043323296, "acc_norm": 0.3881856540084388, "acc_norm_stderr": 0.031722950043323296 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.40358744394618834, "acc_stderr": 0.032928028193303135, "acc_norm": 0.40358744394618834, "acc_norm_stderr": 0.032928028193303135 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.3282442748091603, "acc_stderr": 0.04118438565806299, "acc_norm": 0.3282442748091603, "acc_norm_stderr": 0.04118438565806299 }, "harness|hendrycksTest-international_law|5": { "acc": 0.36363636363636365, "acc_stderr": 0.043913262867240704, "acc_norm": 0.36363636363636365, "acc_norm_stderr": 0.043913262867240704 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.37037037037037035, "acc_stderr": 0.04668408033024931, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.04668408033024931 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.2883435582822086, "acc_stderr": 0.035590395316173425, "acc_norm": 0.2883435582822086, "acc_norm_stderr": 0.035590395316173425 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3392857142857143, "acc_stderr": 0.04493949068613539, "acc_norm": 0.3392857142857143, "acc_norm_stderr": 0.04493949068613539 }, "harness|hendrycksTest-management|5": { "acc": 0.27184466019417475, "acc_stderr": 0.044052680241409216, "acc_norm": 0.27184466019417475, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.3547008547008547, "acc_stderr": 0.03134250486245402, "acc_norm": 0.3547008547008547, "acc_norm_stderr": 0.03134250486245402 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.3269476372924649, "acc_stderr": 0.01677490818013146, "acc_norm": 0.3269476372924649, "acc_norm_stderr": 0.01677490818013146 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.30057803468208094, "acc_stderr": 0.024685316867257792, "acc_norm": 0.30057803468208094, "acc_norm_stderr": 0.024685316867257792 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24581005586592178, "acc_stderr": 0.014400296429225622, "acc_norm": 0.24581005586592178, "acc_norm_stderr": 0.014400296429225622 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2679738562091503, "acc_stderr": 0.025360603796242567, "acc_norm": 0.2679738562091503, "acc_norm_stderr": 0.025360603796242567 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2990353697749196, "acc_stderr": 0.02600330111788514, "acc_norm": 0.2990353697749196, "acc_norm_stderr": 0.02600330111788514 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2993827160493827, "acc_stderr": 0.025483115601195466, "acc_norm": 0.2993827160493827, "acc_norm_stderr": 0.025483115601195466 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.26595744680851063, "acc_stderr": 0.026358065698880596, "acc_norm": 0.26595744680851063, "acc_norm_stderr": 0.026358065698880596 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.24511082138200782, "acc_stderr": 0.010986307870045503, "acc_norm": 0.24511082138200782, "acc_norm_stderr": 0.010986307870045503 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.21323529411764705, "acc_stderr": 0.02488097151229428, "acc_norm": 0.21323529411764705, "acc_norm_stderr": 0.02488097151229428 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2565359477124183, "acc_stderr": 0.017667841612378984, "acc_norm": 0.2565359477124183, "acc_norm_stderr": 0.017667841612378984 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.3, "acc_stderr": 0.04389311454644286, "acc_norm": 0.3, "acc_norm_stderr": 0.04389311454644286 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.22857142857142856, "acc_stderr": 0.026882144922307748, "acc_norm": 0.22857142857142856, "acc_norm_stderr": 0.026882144922307748 }, "harness|hendrycksTest-sociology|5": { "acc": 0.2736318407960199, "acc_stderr": 0.03152439186555401, "acc_norm": 0.2736318407960199, "acc_norm_stderr": 0.03152439186555401 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-virology|5": { "acc": 0.30120481927710846, "acc_stderr": 0.0357160923005348, "acc_norm": 0.30120481927710846, "acc_norm_stderr": 0.0357160923005348 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.3216374269005848, "acc_stderr": 0.03582529442573122, "acc_norm": 0.3216374269005848, "acc_norm_stderr": 0.03582529442573122 }, "harness|truthfulqa:mc|0": { "mc1": 0.23378212974296206, "mc1_stderr": 0.014816195991931586, "mc2": 0.36855840909843307, "mc2_stderr": 0.013989365630749612 }, "harness|winogrande|5": { "acc": 0.6148382004735596, "acc_stderr": 0.013676821287521419 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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 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Junrulu/MT-Bench-Plus
--- license: mit --- A further human-annotated version of [MT Bench](https://arxiv.org/abs/2306.05685): more rounds and long-term questions. Related paper: https://arxiv.org/abs/2308.08239.