davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
Multimodal-Fatima/VQAv2_test_split_4	--- 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: id dtype: int64 - 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: 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: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - 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 splits: - name: test num_bytes: 9135639447.0 num_examples: 44779 download_size: 1867482751 dataset_size: 9135639447.0 --- # Dataset Card for "VQAv2_test_split_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EleutherAI/hendrycks_math	--- license: mit ---
aditnnda/gacoanGenteng	--- license: apache-2.0 ---
ShrinivasSK/en_hi_1	--- dataset_info: features: - name: idx dtype: int64 - name: src dtype: string - name: tgt dtype: string splits: - name: train num_bytes: 6435127.8 num_examples: 18000 - name: test num_bytes: 715014.2 num_examples: 2000 download_size: 3824291 dataset_size: 7150142.0 --- # Dataset Card for "data_hi_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Tippawan/test2-data-semi-trainulb-r3-WLV	--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: int64 - name: prob sequence: float64 - name: ifpass sequence: int64 - name: pred dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 71056510 num_examples: 37243 download_size: 12950153 dataset_size: 71056510 configs: - config_name: default data_files: - split: train path: data/train-* ---
andersonbcdefg/filtered_triples_with_margins	--- dataset_info: features: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string - name: source dtype: string - name: qp_sim dtype: float32 - name: qn_sim dtype: float32 - name: pn_sim dtype: float32 - name: margin dtype: float64 splits: - name: train num_bytes: 1134373304 num_examples: 1246705 download_size: 692171169 dataset_size: 1134373304 configs: - config_name: default data_files: - split: train path: data/train-* ---
Tap-M/Llama2-Uncensored-Orca-Lima-Style	--- license: agpl-3.0 ---
hatakeyama-llm-team/WikiBookJa	--- license: cc-by-3.0 ---
Sharathhebbar24/BeaverTails_filtered	--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - text-generation dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 78049982 num_examples: 161784 download_size: 40376094 dataset_size: 78049982 configs: - config_name: default data_files: - split: train path: data/train-* --- # Beaver Tails This is a cleansed version of [PKU-Alignment/BeaverTails](https://huggingface.co/datasets/PKU-Alignment/BeaverTails) It has two version based on the sensitivity ## Filtered ### Usage ```python from datasets import load_dataset dataset = load_dataset("Sharathhebbar24/BeaverTails_filtered", split="train") ``` ## Unfiltered ### Usage ```python from datasets import load_dataset dataset = load_dataset("Sharathhebbar24/BeaverTails_unfiltered", split="train") ```
mrbrain404/my_datasets	--- license: other ---
elvis-d/train	--- license: apache-2.0 ---
mask-distilled-one-sec-cv12/chunk_165	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1171949260 num_examples: 230155 download_size: 1197293781 dataset_size: 1171949260 --- # Dataset Card for "chunk_165" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BelalElhossany/mgb2_audios_transcriptions	--- dataset_info: features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 1181823173.4 num_examples: 5842 download_size: 1141789958 dataset_size: 1181823173.4 --- # Dataset Card for "mgb2_audios_transcriptions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceM4/VQAv2_modif-Dummy	Invalid username or password.
Duskfallcrew/Alex_Brightman	--- license: creativeml-openrail-m task_categories: - text-to-image language: - en tags: - lora - lycoris pretty_name: Alex Brightman size_categories: - 1K<n<10K --- Alex Brightman Lycoris
mnoukhov/openai_summarize_tldr_rbaseline	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: prompt dtype: string - name: label dtype: string - name: reward_baseline dtype: float32 splits: - name: train num_bytes: 181611007 num_examples: 116722 - name: valid num_bytes: 3120685 num_examples: 2000 download_size: 113145941 dataset_size: 184731692 --- # Dataset Card for "openai_summarize_tldr_rbaseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deepapaikar/Sentence_pairs	--- license: apache-2.0 dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 1901011 num_examples: 12560 download_size: 991351 dataset_size: 1901011 configs: - config_name: default data_files: - split: train path: data/train-* ---
Ediudo/sal	--- license: openrail ---
DBQ/Fendi.Standard.Categories.Italy	--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Italy - Fendi - Fashion Standard Categories tags: - webscraping - ecommerce - Fendi - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: dbq_prd_type dtype: string - name: competence_date dtype: string - name: website_name dtype: string - name: product_code dtype: string - name: tag_field dtype: string splits: - name: train num_bytes: 271821 num_examples: 3055 download_size: 56203 dataset_size: 271821 --- # Fendi web scraped data ## About the website In the EMEA region, particularly in Italy, the luxury fashion industry has an immense influence and it significantly contributes to Italys economy. Brands such as Fendi are prominent players in this sector. Emphasizing on haute couture, ready-to-wear clothing, leather goods, shoes, fragrances, eyewear, timepieces and accessories, the industry has seen significant growth with the adoption of Ecommerce. Our dataset provides an insightful look at Ecommerce product tags data on Fendi products in Italy. The research data aids in understanding the online buying behavior of luxury fashion consumers in Italy, providing great potential for market analysis and strategy development. ## Link to dataset [Italy - Fendi - Fashion Standard Categories dataset](https://www.databoutique.com/buy-data-page/Fendi%20Standard%20Categories%20Italy/r/recrd9vOREnRQ68V1)
rgricardo/Takubgroup	--- license: openrail ---
Susmita1302/images	--- license: mit ---
vadimpetrov/common_voice_13_0_ru_pseudo_labelled	--- dataset_info: config_name: ru features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 991934032.405 num_examples: 26269 - name: validation num_bytes: 401622741.215 num_examples: 10185 - name: test num_bytes: 391004723.912 num_examples: 10186 download_size: 1724461730 dataset_size: 1784561497.5319998 configs: - config_name: ru data_files: - split: train path: ru/train-* - split: validation path: ru/validation-* - split: test path: ru/test-* ---
cvzion/dataset-dqg1	--- dataset_info: features: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 57020 num_examples: 238 download_size: 16985 dataset_size: 57020 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/idw_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of idw/IDW/IDW (Girls' Frontline) This is the dataset of idw/IDW/IDW (Girls' Frontline), containing 125 images and their tags. The core tags of this character are `animal_ears, blonde_hair, cat_ears, blue_eyes, long_hair, twintails, hair_ornament, bangs, hair_between_eyes, tail, fang, hairclip, cat_tail, breasts, animal_ear_fluff`, 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 \| 125 \| 142.10 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 125 \| 82.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 295 \| 177.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 125 \| 126.03 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 295 \| 249.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/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/idw_girlsfrontline', 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 \| 8 \| ![](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, fingerless_gloves, shorts, boots, headset, looking_at_viewer, necktie, open_mouth, suspenders, holding_gun, knee_pads, smile, full_body, panties, submachine_gun, :3, sleeves_rolled_up, white_shirt \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, fingerless_gloves, headset, open_mouth, suspender_shorts, collared_shirt, white_shirt, black_shorts, holding_gun, sleeves_rolled_up, :d, blue_panties, character_name, cowboy_shot, short_shorts, submachine_gun, 2girls, looking_at_viewer, solo_focus, striped_necktie, v-shaped_eyebrows \| \| 2 \| 11 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, elbow_gloves, solo, black_dress, black_gloves, official_alternate_costume, open_mouth, smile, bare_shoulders, choker, small_breasts, collarbone, looking_at_viewer, strapless_dress, sunglasses, simple_background, thigh_strap, :3, gun, tail_ribbon, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| fingerless_gloves \| shorts \| boots \| headset \| looking_at_viewer \| necktie \| open_mouth \| suspenders \| holding_gun \| knee_pads \| smile \| full_body \| panties \| submachine_gun \| :3 \| sleeves_rolled_up \| white_shirt \| suspender_shorts \| collared_shirt \| black_shorts \| :d \| blue_panties \| character_name \| cowboy_shot \| short_shorts \| 2girls \| solo_focus \| striped_necktie \| v-shaped_eyebrows \| elbow_gloves \| black_dress \| black_gloves \| official_alternate_costume \| bare_shoulders \| choker \| small_breasts \| collarbone \| strapless_dress \| sunglasses \| simple_background \| thigh_strap \| gun \| tail_ribbon \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:---------\|:--------\|:----------\|:--------------------\|:----------\|:-------------\|:-------------\|:--------------\|:------------\|:--------\|:------------\|:----------\|:-----------------\|:-----\|:--------------------\|:--------------\|:-------------------\|:-----------------\|:---------------\|:-----\|:---------------\|:-----------------\|:--------------\|:---------------\|:---------\|:-------------\|:------------------\|:--------------------\|:---------------\|:--------------\|:---------------\|:-----------------------------\|:-----------------\|:---------\|:----------------\|:-------------\|:------------------\|:-------------\|:--------------------\|:--------------\|:------\|:--------------\|:-------------------\| \| 0 \| 8 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| X \| \| \| X \| X \| \| X \| \| X \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 11 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| \| \| \| X \| \| X \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
timm/imagenet-12k-wds	--- license: other license_name: imagenet license_link: https://www.image-net.org/download.php task_categories: - image-classification pretty_name: ImageNet-12k size_categories: - 10M<n<100M extra_gated_prompt: >- By clicking on “Access repository” below, you also agree to ImageNet Terms of Access: [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement. tags: - webdataset --- ## Dataset Description - Homepage: https://image-net.org/index.php - Repository: https://github.com/rwightman/imagenet-12k - Paper: https://arxiv.org/abs/1409.0575 ### Dataset Summary This is a filtered copy of the full [ImageNet](https://www.image-net.org/) dataset consisting of the top 11821 (of 21841) classes by number of samples. It has been used to pretrain a number of `in12k` models in `timm`. The code and metadata for building this dataset from the original full ImageNet can be found at https://github.com/rwightman/imagenet-12k NOTE: This subset was filtered from the original `fall11` ImageNet release which has been replaced by the `winter21` release which removes close to 3000 synsets containing people, a number of these are of an offensive or sensitive nature. There is work in progress to filter a similar dataset from `winter21`, and there is already [ImageNet-21k-P](https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_instructions.md) but with different thresholds & preprocessing steps. ### Data Splits Unlike ImageNet-1k (ILSVRC 2012), the full ImageNet dataset has no defined splits. This subset includes a validation split consiting of 40 samples per 11821 classes. #### Train * `imagenet12k-train-{0000..1023}.tar` * 12129687 samples over 1024 shards #### Validation * `imagenet12k-validation-{0000..0511}.tar` * 472840 samples over 512 shards ### Processing I performed some processing while sharding this dataset: * All exif tags not related to color space were removed * All images with width or height < 48 were removed. * All images with the smallest edge > 600 were resized, maintaining aspect so that they were = 600. Improving size & decoding time uniformity for typical pretrain use cases. * Images were pre-shuffled across the shards ## Additional Information ### Dataset Curators Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848): - Olga Russakovsky - Jia Deng - Hao Su - Jonathan Krause - Sanjeev Satheesh - Wei Dong - Richard Socher - Li-Jia Li - Kai Li - Sean Ma - Zhiheng Huang - Andrej Karpathy - Aditya Khosla - Michael Bernstein - Alexander C Berg - Li Fei-Fei ### Licensing Information In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 1. The law of the State of New Jersey shall apply to all disputes under this agreement. ### Citation Information ```bibtex @article{imagenet15russakovsky, Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, Title = { {ImageNet Large Scale Visual Recognition Challenge} }, Year = {2015}, journal = {International Journal of Computer Vision (IJCV)}, doi = {10.1007/s11263-015-0816-y}, volume={115}, number={3}, pages={211-252} } ```
RKnijn/ds-llama2	--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: source dtype: string splits: - name: train num_bytes: 51117417 num_examples: 126287 download_size: 26326368 dataset_size: 51117417 configs: - config_name: default data_files: - split: train path: data/train-* ---
daze-unlv/medmcqa-mistral-FT-1000	--- license: apache-2.0 ---
Mariofm02/cards_images	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ace of clubs '1': ace of diamonds '2': ace of hearts '3': ace of spades '4': eight of clubs '5': eight of diamonds '6': eight of hearts '7': eight of spades '8': five of clubs '9': five of diamonds '10': five of hearts '11': five of spades '12': four of clubs '13': four of diamonds '14': four of hearts '15': four of spades '16': jack of clubs '17': jack of diamonds '18': jack of hearts '19': jack of spades '20': king of clubs '21': king of diamonds '22': king of hearts '23': king of spades '24': nine of clubs '25': nine of diamonds '26': nine of hearts '27': nine of spades '28': queen of clubs '29': queen of diamonds '30': queen of hearts '31': queen of spades '32': seven of clubs '33': seven of diamonds '34': seven of hearts '35': seven of spades '36': six of clubs '37': six of diamonds '38': six of hearts '39': six of spades '40': ten of clubs '41': ten of diamonds '42': ten of hearts '43': ten of spades '44': three of clubs '45': three of diamonds '46': three of hearts '47': three of spades '48': two of clubs '49': two of diamonds '50': two of hearts '51': two of spades splits: - name: train num_bytes: 130695849.8739789 num_examples: 6603 - name: test num_bytes: 23038296.14202111 num_examples: 1166 download_size: 150881036 dataset_size: 153734146.016 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
rlinares2/chatbot_arena_embeddings_adav3	--- dataset_info: features: - name: question_embedding sequence: float64 - name: answer_embeddings_a sequence: float64 - name: answer_embeddings_b sequence: float64 splits: - name: train num_bytes: 885024000 num_examples: 24000 download_size: 647518122 dataset_size: 885024000 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_steve-cse__MelloGPT	--- pretty_name: Evaluation run of steve-cse/MelloGPT dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [steve-cse/MelloGPT](https://huggingface.co/steve-cse/MelloGPT) 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_steve-cse__MelloGPT\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T18:07:59.704375](https://huggingface.co/datasets/open-llm-leaderboard/details_steve-cse__MelloGPT/blob/main/results_2023-12-16T18-07-59.704375.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\": 0.5580941639624716,\n\ \ \"acc_stderr\": 0.03400113351875304,\n \"acc_norm\": 0.5629530798562845,\n\ \ \"acc_norm_stderr\": 0.03471812426856797,\n \"mc1\": 0.38555691554467564,\n\ \ \"mc1_stderr\": 0.017038839010591673,\n \"mc2\": 0.5560955080575972,\n\ \ \"mc2_stderr\": 0.014788612119825833\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5247440273037542,\n \"acc_stderr\": 0.014593487694937738,\n\ \ \"acc_norm\": 0.53839590443686,\n \"acc_norm_stderr\": 0.01456824555029636\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5646285600477993,\n\ \ \"acc_stderr\": 0.004947922692688834,\n \"acc_norm\": 0.761202947619996,\n\ \ \"acc_norm_stderr\": 0.004254771367531346\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4074074074074074,\n\ \ \"acc_stderr\": 0.04244633238353229,\n \"acc_norm\": 0.4074074074074074,\n\ \ \"acc_norm_stderr\": 0.04244633238353229\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5460526315789473,\n \"acc_stderr\": 0.04051646342874142,\n\ \ \"acc_norm\": 0.5460526315789473,\n \"acc_norm_stderr\": 0.04051646342874142\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.49,\n\ \ \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \ \ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.030000485448675986,\n\ \ \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.030000485448675986\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\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5549132947976878,\n\ \ \"acc_stderr\": 0.03789401760283648,\n \"acc_norm\": 0.5549132947976878,\n\ \ \"acc_norm_stderr\": 0.03789401760283648\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.3508771929824561,\n\ \ \"acc_stderr\": 0.04489539350270699,\n \"acc_norm\": 0.3508771929824561,\n\ \ \"acc_norm_stderr\": 0.04489539350270699\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155254,\n \"\ acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155254\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.36507936507936506,\n\ \ \"acc_stderr\": 0.043062412591271526,\n \"acc_norm\": 0.36507936507936506,\n\ \ \"acc_norm_stderr\": 0.043062412591271526\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\"\ : 0.6548387096774193,\n \"acc_stderr\": 0.02704574657353433,\n \"\ acc_norm\": 0.6548387096774193,\n \"acc_norm_stderr\": 0.02704574657353433\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.43349753694581283,\n \"acc_stderr\": 0.03486731727419872,\n \"\ acc_norm\": 0.43349753694581283,\n \"acc_norm_stderr\": 0.03486731727419872\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.03663974994391244,\n\ \ \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.03663974994391244\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"\ acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7409326424870466,\n \"acc_stderr\": 0.031618779179354115,\n\ \ \"acc_norm\": 0.7409326424870466,\n \"acc_norm_stderr\": 0.031618779179354115\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5307692307692308,\n \"acc_stderr\": 0.025302958890850154,\n\ \ \"acc_norm\": 0.5307692307692308,\n \"acc_norm_stderr\": 0.025302958890850154\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815635,\n \ \ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815635\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5630252100840336,\n \"acc_stderr\": 0.03221943636566196,\n \ \ \"acc_norm\": 0.5630252100840336,\n \"acc_norm_stderr\": 0.03221943636566196\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\"\ : 0.7174311926605504,\n \"acc_stderr\": 0.019304243497707152,\n \"\ acc_norm\": 0.7174311926605504,\n \"acc_norm_stderr\": 0.019304243497707152\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.48148148148148145,\n \"acc_stderr\": 0.03407632093854052,\n \"\ acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.03407632093854052\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.6764705882352942,\n \"acc_stderr\": 0.032834720561085606,\n \"\ acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.032834720561085606\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.679324894514768,\n \"acc_stderr\": 0.0303819319499904,\n \ \ \"acc_norm\": 0.679324894514768,\n \"acc_norm_stderr\": 0.0303819319499904\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6367713004484304,\n\ \ \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n\ \ \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847834,\n\ \ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847834\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6363636363636364,\n \"acc_stderr\": 0.043913262867240704,\n \"\ acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.043913262867240704\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6851851851851852,\n\ \ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\ \ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n\ \ \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489122,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489122\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.04656147110012351,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.04656147110012351\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077788,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077788\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526094\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7471264367816092,\n\ \ \"acc_stderr\": 0.015543377313719681,\n \"acc_norm\": 0.7471264367816092,\n\ \ \"acc_norm_stderr\": 0.015543377313719681\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5982658959537572,\n \"acc_stderr\": 0.026394104177643637,\n\ \ \"acc_norm\": 0.5982658959537572,\n \"acc_norm_stderr\": 0.026394104177643637\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.25251396648044694,\n\ \ \"acc_stderr\": 0.014530330201468647,\n \"acc_norm\": 0.25251396648044694,\n\ \ \"acc_norm_stderr\": 0.014530330201468647\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6405228758169934,\n \"acc_stderr\": 0.027475969910660952,\n\ \ \"acc_norm\": 0.6405228758169934,\n \"acc_norm_stderr\": 0.027475969910660952\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6141479099678456,\n\ \ \"acc_stderr\": 0.027648149599751464,\n \"acc_norm\": 0.6141479099678456,\n\ \ \"acc_norm_stderr\": 0.027648149599751464\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.027339546640662734,\n\ \ \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.027339546640662734\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.375886524822695,\n \"acc_stderr\": 0.028893955412115882,\n \ \ \"acc_norm\": 0.375886524822695,\n \"acc_norm_stderr\": 0.028893955412115882\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.39765319426336376,\n\ \ \"acc_stderr\": 0.012499840347460642,\n \"acc_norm\": 0.39765319426336376,\n\ \ \"acc_norm_stderr\": 0.012499840347460642\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.030161911930767102,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.030161911930767102\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5277777777777778,\n \"acc_stderr\": 0.020196594933541194,\n \ \ \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.020196594933541194\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6530612244897959,\n \"acc_stderr\": 0.030472526026726492,\n\ \ \"acc_norm\": 0.6530612244897959,\n \"acc_norm_stderr\": 0.030472526026726492\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7611940298507462,\n\ \ \"acc_stderr\": 0.03014777593540922,\n \"acc_norm\": 0.7611940298507462,\n\ \ \"acc_norm_stderr\": 0.03014777593540922\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5,\n \ \ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.03892494720807614\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7309941520467836,\n \"acc_stderr\": 0.03401052620104089,\n\ \ \"acc_norm\": 0.7309941520467836,\n \"acc_norm_stderr\": 0.03401052620104089\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.38555691554467564,\n\ \ \"mc1_stderr\": 0.017038839010591673,\n \"mc2\": 0.5560955080575972,\n\ \ \"mc2_stderr\": 0.014788612119825833\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7387529597474349,\n \"acc_stderr\": 0.012346914863415314\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3009855951478393,\n \ \ \"acc_stderr\": 0.0126345044652112\n }\n}\n```" repo_url: https://huggingface.co/steve-cse/MelloGPT 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_16T18_07_59.704375 path: - '/details_harness\|arc:challenge\|25_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-16T18-07-59.704375.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|gsm8k\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hellaswag\|10_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T18-07-59.704375.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T18-07-59.704375.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '/details_harness\|winogrande\|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-16T18-07-59.704375.parquet' - config_name: results data_files: - split: 2023_12_16T18_07_59.704375 path: - results_2023-12-16T18-07-59.704375.parquet - split: latest path: - results_2023-12-16T18-07-59.704375.parquet --- # Dataset Card for Evaluation run of steve-cse/MelloGPT <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [steve-cse/MelloGPT](https://huggingface.co/steve-cse/MelloGPT) 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_steve-cse__MelloGPT", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T18:07:59.704375](https://huggingface.co/datasets/open-llm-leaderboard/details_steve-cse__MelloGPT/blob/main/results_2023-12-16T18-07-59.704375.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": 0.5580941639624716, "acc_stderr": 0.03400113351875304, "acc_norm": 0.5629530798562845, "acc_norm_stderr": 0.03471812426856797, "mc1": 0.38555691554467564, "mc1_stderr": 0.017038839010591673, "mc2": 0.5560955080575972, "mc2_stderr": 0.014788612119825833 }, "harness\|arc:challenge\|25": { "acc": 0.5247440273037542, "acc_stderr": 0.014593487694937738, "acc_norm": 0.53839590443686, "acc_norm_stderr": 0.01456824555029636 }, "harness\|hellaswag\|10": { "acc": 0.5646285600477993, "acc_stderr": 0.004947922692688834, "acc_norm": 0.761202947619996, "acc_norm_stderr": 0.004254771367531346 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.04244633238353229, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.04244633238353229 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5460526315789473, "acc_stderr": 0.04051646342874142, "acc_norm": 0.5460526315789473, "acc_norm_stderr": 0.04051646342874142 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6113207547169811, "acc_stderr": 0.030000485448675986, "acc_norm": 0.6113207547169811, "acc_norm_stderr": 0.030000485448675986 }, "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": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5549132947976878, "acc_stderr": 0.03789401760283648, "acc_norm": 0.5549132947976878, "acc_norm_stderr": 0.03789401760283648 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.30392156862745096, "acc_stderr": 0.045766654032077636, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.045766654032077636 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.03267862331014063, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.03267862331014063 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3508771929824561, "acc_stderr": 0.04489539350270699, "acc_norm": 0.3508771929824561, "acc_norm_stderr": 0.04489539350270699 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155254, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155254 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.36507936507936506, "acc_stderr": 0.043062412591271526, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.043062412591271526 }, "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": 0.6548387096774193, "acc_stderr": 0.02704574657353433, "acc_norm": 0.6548387096774193, "acc_norm_stderr": 0.02704574657353433 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.43349753694581283, "acc_stderr": 0.03486731727419872, "acc_norm": 0.43349753694581283, "acc_norm_stderr": 0.03486731727419872 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.03663974994391244, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.03663974994391244 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.03173071239071724, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.03173071239071724 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7409326424870466, "acc_stderr": 0.031618779179354115, "acc_norm": 0.7409326424870466, "acc_norm_stderr": 0.031618779179354115 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5307692307692308, "acc_stderr": 0.025302958890850154, "acc_norm": 0.5307692307692308, "acc_norm_stderr": 0.025302958890850154 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815635, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815635 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5630252100840336, "acc_stderr": 0.03221943636566196, "acc_norm": 0.5630252100840336, "acc_norm_stderr": 0.03221943636566196 }, "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": 0.7174311926605504, "acc_stderr": 0.019304243497707152, "acc_norm": 0.7174311926605504, "acc_norm_stderr": 0.019304243497707152 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.48148148148148145, "acc_stderr": 0.03407632093854052, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.03407632093854052 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.032834720561085606, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.032834720561085606 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.679324894514768, "acc_stderr": 0.0303819319499904, "acc_norm": 0.679324894514768, "acc_norm_stderr": 0.0303819319499904 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6367713004484304, "acc_stderr": 0.032277904428505, "acc_norm": 0.6367713004484304, "acc_norm_stderr": 0.032277904428505 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7251908396946565, "acc_stderr": 0.03915345408847834, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.03915345408847834 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.043913262867240704, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.043913262867240704 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6851851851851852, "acc_stderr": 0.04489931073591312, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.04489931073591312 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6809815950920245, "acc_stderr": 0.03661997551073836, "acc_norm": 0.6809815950920245, "acc_norm_stderr": 0.03661997551073836 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489122, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489122 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6699029126213593, "acc_stderr": 0.04656147110012351, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.04656147110012351 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077788, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077788 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.67, "acc_stderr": 0.047258156262526094, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526094 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7471264367816092, "acc_stderr": 0.015543377313719681, "acc_norm": 0.7471264367816092, "acc_norm_stderr": 0.015543377313719681 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5982658959537572, "acc_stderr": 0.026394104177643637, "acc_norm": 0.5982658959537572, "acc_norm_stderr": 0.026394104177643637 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.25251396648044694, "acc_stderr": 0.014530330201468647, "acc_norm": 0.25251396648044694, "acc_norm_stderr": 0.014530330201468647 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6405228758169934, "acc_stderr": 0.027475969910660952, "acc_norm": 0.6405228758169934, "acc_norm_stderr": 0.027475969910660952 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6141479099678456, "acc_stderr": 0.027648149599751464, "acc_norm": 0.6141479099678456, "acc_norm_stderr": 0.027648149599751464 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5925925925925926, "acc_stderr": 0.027339546640662734, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.027339546640662734 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.375886524822695, "acc_stderr": 0.028893955412115882, "acc_norm": 0.375886524822695, "acc_norm_stderr": 0.028893955412115882 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.39765319426336376, "acc_stderr": 0.012499840347460642, "acc_norm": 0.39765319426336376, "acc_norm_stderr": 0.012499840347460642 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5588235294117647, "acc_stderr": 0.030161911930767102, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.030161911930767102 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.020196594933541194, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.020196594933541194 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6530612244897959, "acc_stderr": 0.030472526026726492, "acc_norm": 0.6530612244897959, "acc_norm_stderr": 0.030472526026726492 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7611940298507462, "acc_stderr": 0.03014777593540922, "acc_norm": 0.7611940298507462, "acc_norm_stderr": 0.03014777593540922 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5, "acc_stderr": 0.03892494720807614, "acc_norm": 0.5, "acc_norm_stderr": 0.03892494720807614 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7309941520467836, "acc_stderr": 0.03401052620104089, "acc_norm": 0.7309941520467836, "acc_norm_stderr": 0.03401052620104089 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.38555691554467564, "mc1_stderr": 0.017038839010591673, "mc2": 0.5560955080575972, "mc2_stderr": 0.014788612119825833 }, "harness\|winogrande\|5": { "acc": 0.7387529597474349, "acc_stderr": 0.012346914863415314 }, "harness\|gsm8k\|5": { "acc": 0.3009855951478393, "acc_stderr": 0.0126345044652112 } } ``` ## 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. 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diogofouto/dialogsum-augmented	--- license: apache-2.0 --- # DialogSum Enhanced Dataset ## Overview DialogSum Enhanced is an extension of the original DialogSum dataset, enriched with a new column called 'Relevant Sentences.' This dataset is designed to facilitate research in dialogue summarization by providing additional information about the dialogue turns that GPT-4 considers relevant for generating summaries. ### Changes from DialogSum The primary enhancement in DialogSum Enhanced is the inclusion of the 'Relevant Sentences' column. This column contains the dialogue turns that GPT-4 identified as crucial for the generation of a summary. This information can be valuable for understanding the model's decision-making process and improving dialogue summarization models. ### Split Information - Train Split: The train split in DialogSum Enhanced consists of half of the original DialogSum train split. - Test and Validation Sets: The test and validation sets in DialogSum Enhanced retain their full length from the original DialogSum dataset. ## Dataset Structure The dataset is provided in a CSV format with the following columns: 1. id: Unique identifier for each dialogue. 2. dialogue: The sequential turns of the dialogue. 3. relevant_sentences: The dialogue turns that GPT-4 considered relevant for generating the summary. 4. summary: The reference summary for the dialogue. ## Usage Researchers and practitioners interested in dialogue summarization can leverage DialogSum Enhanced for training, validating, and testing their models. The 'Relevant Sentences' column provides additional insights into the model's decision-making process during summarization.
atifss/gpt2-124M-qlora-chat-support	--- dataset_info: features: - name: answer dtype: string - name: question dtype: string splits: - name: train num_bytes: 17924 num_examples: 79 download_size: 9896 dataset_size: 17924 --- # Dataset Card for "gpt2-124M-qlora-chat-support" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1	--- pretty_name: Evaluation run of ZoidBB/unraveled-7b-a1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ZoidBB/unraveled-7b-a1](https://huggingface.co/ZoidBB/unraveled-7b-a1) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-23T19:22:53.071269](https://huggingface.co/datasets/open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1_public/blob/main/results_2023-11-23T19-22-53.071269.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\": 0.627016601295214,\n\ \ \"acc_stderr\": 0.03235619334922418,\n \"acc_norm\": 0.6365794887378388,\n\ \ \"acc_norm_stderr\": 0.03306927475699416,\n \"mc1\": 0.28151774785801714,\n\ \ \"mc1_stderr\": 0.01574402724825605,\n \"mc2\": 0.42228384526614654,\n\ \ \"mc2_stderr\": 0.014152177395393957,\n \"em\": 0.0017827181208053692,\n\ \ \"em_stderr\": 0.00043200973460387867,\n \"f1\": 0.06056837248322149,\n\ \ \"f1_stderr\": 0.0013671084143061485\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5648464163822525,\n \"acc_stderr\": 0.014487986197186045,\n\ \ \"acc_norm\": 0.5981228668941979,\n \"acc_norm_stderr\": 0.014327268614578274\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.635929097789285,\n\ \ \"acc_stderr\": 0.004801852881329739,\n \"acc_norm\": 0.8280223063134834,\n\ \ \"acc_norm_stderr\": 0.0037658983649388736\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.04244633238353227\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.039105257528497236,\n\ \ \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.039105257528497236\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7291666666666666,\n\ \ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\ \ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5862068965517241,\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\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\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\"\ : 0.7580645161290323,\n \"acc_stderr\": 0.024362599693031096,\n \"\ acc_norm\": 0.7580645161290323,\n \"acc_norm_stderr\": 0.024362599693031096\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4827586206896552,\n \"acc_stderr\": 0.035158955511657,\n \"acc_norm\"\ : 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511657\n },\n\ \ \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\"\ : 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026704,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026704\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206865,\n \ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206865\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.039439666991836285,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.039439666991836285\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8073394495412844,\n \"acc_stderr\": 0.016909276884936073,\n \"\ acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.016909276884936073\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5787037037037037,\n \"acc_stderr\": 0.033674621388960775,\n \"\ acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.033674621388960775\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.0286265479124374,\n \"acc_norm\"\ : 0.7892156862745098,\n \"acc_norm_stderr\": 0.0286265479124374\n },\n\ \ \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\":\ \ 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676173,\n \"\ acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676173\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7129629629629629,\n\ \ \"acc_stderr\": 0.04373313040914761,\n \"acc_norm\": 0.7129629629629629,\n\ \ \"acc_norm_stderr\": 0.04373313040914761\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.03157065078911901,\n\ \ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.03157065078911901\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\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092368,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092368\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.013890862162876163,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.013890862162876163\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.708092485549133,\n \"acc_stderr\": 0.024476994076247333,\n\ \ \"acc_norm\": 0.708092485549133,\n \"acc_norm_stderr\": 0.024476994076247333\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3039106145251397,\n\ \ \"acc_stderr\": 0.015382845587584517,\n \"acc_norm\": 0.3039106145251397,\n\ \ \"acc_norm_stderr\": 0.015382845587584517\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n\ \ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.684887459807074,\n\ \ \"acc_stderr\": 0.026385273703464485,\n \"acc_norm\": 0.684887459807074,\n\ \ \"acc_norm_stderr\": 0.026385273703464485\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.02517104191530968,\n\ \ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.02517104191530968\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303062,\n \ \ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303062\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4426336375488918,\n\ \ \"acc_stderr\": 0.012685906538206247,\n \"acc_norm\": 0.4426336375488918,\n\ \ \"acc_norm_stderr\": 0.012685906538206247\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6437908496732027,\n \"acc_stderr\": 0.0193733324207245,\n \ \ \"acc_norm\": 0.6437908496732027,\n \"acc_norm_stderr\": 0.0193733324207245\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.28151774785801714,\n\ \ \"mc1_stderr\": 0.01574402724825605,\n \"mc2\": 0.42228384526614654,\n\ \ \"mc2_stderr\": 0.014152177395393957\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7719021310181531,\n \"acc_stderr\": 0.011793015817663592\n\ \ },\n \"harness\|drop\|3\": {\n \"em\": 0.0017827181208053692,\n \ \ \"em_stderr\": 0.00043200973460387867,\n \"f1\": 0.06056837248322149,\n\ \ \"f1_stderr\": 0.0013671084143061485\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.14329037149355572,\n \"acc_stderr\": 0.009650895723357585\n\ \ }\n}\n```" repo_url: https://huggingface.co/ZoidBB/unraveled-7b-a1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|arc:challenge\|25_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-11-23T19-22-53.071269.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|drop\|3_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-11-23T19-22-53.071269.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|gsm8k\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hellaswag\|10_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-11-23T19-22-53.071269.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-management\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-virology\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|truthfulqa:mc\|0_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-11-23T19-22-53.071269.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '/details_harness\|winogrande\|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-11-23T19-22-53.071269.parquet' - config_name: results data_files: - split: 2023_11_23T19_22_53.071269 path: - results_2023-11-23T19-22-53.071269.parquet - split: latest path: - results_2023-11-23T19-22-53.071269.parquet --- # Dataset Card for Evaluation run of ZoidBB/unraveled-7b-a1 ## Dataset Description - Homepage: - Repository: https://huggingface.co/ZoidBB/unraveled-7b-a1 - 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 [ZoidBB/unraveled-7b-a1](https://huggingface.co/ZoidBB/unraveled-7b-a1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-23T19:22:53.071269](https://huggingface.co/datasets/open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1_public/blob/main/results_2023-11-23T19-22-53.071269.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": 0.627016601295214, "acc_stderr": 0.03235619334922418, "acc_norm": 0.6365794887378388, "acc_norm_stderr": 0.03306927475699416, "mc1": 0.28151774785801714, "mc1_stderr": 0.01574402724825605, "mc2": 0.42228384526614654, "mc2_stderr": 0.014152177395393957, "em": 0.0017827181208053692, "em_stderr": 0.00043200973460387867, "f1": 0.06056837248322149, "f1_stderr": 0.0013671084143061485 }, "harness\|arc:challenge\|25": { "acc": 0.5648464163822525, "acc_stderr": 0.014487986197186045, "acc_norm": 0.5981228668941979, "acc_norm_stderr": 0.014327268614578274 }, "harness\|hellaswag\|10": { "acc": 0.635929097789285, "acc_stderr": 0.004801852881329739, "acc_norm": 0.8280223063134834, "acc_norm_stderr": 0.0037658983649388736 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5925925925925926, "acc_stderr": 0.04244633238353227, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.04244633238353227 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6381578947368421, "acc_stderr": 0.039105257528497236, "acc_norm": 0.6381578947368421, "acc_norm_stderr": 0.039105257528497236 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566017, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566017 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7580645161290323, "acc_stderr": 0.024362599693031096, "acc_norm": 0.7580645161290323, "acc_norm_stderr": 0.024362599693031096 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511657, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511657 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7929292929292929, "acc_stderr": 0.02886977846026704, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026704 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758733, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758733 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.362962962962963, "acc_stderr": 0.029318203645206865, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.029318203645206865 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.634453781512605, "acc_stderr": 0.031282177063684614, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.031282177063684614 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.039439666991836285, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.039439666991836285 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8073394495412844, "acc_stderr": 0.016909276884936073, "acc_norm": 0.8073394495412844, "acc_norm_stderr": 0.016909276884936073 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5787037037037037, "acc_stderr": 0.033674621388960775, "acc_norm": 0.5787037037037037, "acc_norm_stderr": 0.033674621388960775 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.0286265479124374, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.0286265479124374 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676173, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676173 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6681614349775785, "acc_stderr": 0.031602951437766785, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.031602951437766785 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7129629629629629, "acc_stderr": 0.04373313040914761, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.04373313040914761 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7975460122699386, "acc_stderr": 0.03157065078911901, "acc_norm": 0.7975460122699386, "acc_norm_stderr": 0.03157065078911901 }, "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": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092368, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092368 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.013890862162876163, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.013890862162876163 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.708092485549133, "acc_stderr": 0.024476994076247333, "acc_norm": 0.708092485549133, "acc_norm_stderr": 0.024476994076247333 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3039106145251397, "acc_stderr": 0.015382845587584517, "acc_norm": 0.3039106145251397, "acc_norm_stderr": 0.015382845587584517 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7450980392156863, "acc_stderr": 0.02495418432487991, "acc_norm": 0.7450980392156863, "acc_norm_stderr": 0.02495418432487991 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.684887459807074, "acc_stderr": 0.026385273703464485, "acc_norm": 0.684887459807074, "acc_norm_stderr": 0.026385273703464485 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7129629629629629, "acc_stderr": 0.02517104191530968, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.02517104191530968 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4716312056737589, "acc_stderr": 0.029779450957303062, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.029779450957303062 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4426336375488918, "acc_stderr": 0.012685906538206247, "acc_norm": 0.4426336375488918, "acc_norm_stderr": 0.012685906538206247 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462923, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462923 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6437908496732027, "acc_stderr": 0.0193733324207245, "acc_norm": 0.6437908496732027, "acc_norm_stderr": 0.0193733324207245 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482706, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482706 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.28151774785801714, "mc1_stderr": 0.01574402724825605, "mc2": 0.42228384526614654, "mc2_stderr": 0.014152177395393957 }, "harness\|winogrande\|5": { "acc": 0.7719021310181531, "acc_stderr": 0.011793015817663592 }, "harness\|drop\|3": { "em": 0.0017827181208053692, "em_stderr": 0.00043200973460387867, "f1": 0.06056837248322149, "f1_stderr": 0.0013671084143061485 }, "harness\|gsm8k\|5": { "acc": 0.14329037149355572, "acc_stderr": 0.009650895723357585 } } ``` ### 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]
WangX0111/MLIR-Passes	--- license: mit --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary MLIR file associated passes, crawled from the official LLVM/MLIR repository. ### Supported Tasks and Leaderboards Code LLM for MLIR optimization ### Languages MLIR
Oivalf23/jhony	--- license: openrail ---
flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - multilingual pretty_name: stackexchange size_categories: - unknown source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa --- # Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers)s - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [stackexchange](https://archive.org/details/stackexchange) - Repository: [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings) ### Dataset Summary We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites). ### Languages Stack Exchange mainly consist of english language (en). ## Dataset Structure ### Data Instances Each data samples is presented as follow: ``` {'title_body': 'How to determine if 3 points on a 3-D graph are collinear? Let the points $A, B$ and $C$ be $(x_1, y_1, z_1), (x_2, y_2, z_2)$ and $(x_3, y_3, z_3)$ respectively. How do I prove that the 3 points are collinear? What is the formula?', 'upvoted_answer': 'From $A(x_1,y_1,z_1),B(x_2,y_2,z_2),C(x_3,y_3,z_3)$ we can get their position vectors.\n\n$\\vec{AB}=(x_2-x_1,y_2-y_1,z_2-z_1)$ and $\\vec{AC}=(x_3-x_1,y_3-y_1,z_3-z_1)$.\n\nThen $\|\|\\vec{AB}\\times\\vec{AC}\|\|=0\\implies A,B,C$ collinear.', ``` This particular exampe corresponds to the [following page](https://math.stackexchange.com/questions/947555/how-to-determine-if-3-points-on-a-3-d-graph-are-collinear) ### Data Fields The fields present in the dataset contain the following informations: - `title_body`: This is the concatenation of the title and body from the question - `upvoted_answer`: This is the body from the most upvoted answer ### Data Splits We provide multiple splits for this dataset, which each refers to a given community channel. We detail the number of pail for each split below: \| \| Number of pairs \| \| ----- \| ------ \| \| apple \| 92,487 \| \| english \| 100,640 \| \| codereview \| 41,748 \| \| dba \| 71,449 \| \| mathoverflow \| 85,289 \| \| electronics \| 129,494 \| \| mathematica \| 59,895 \| \| drupal \| 67,817 \| \| magento \| 79,241 \| \| gaming \| 82,887 \| \| ell \| 77,892 \| \| gamedev \| 40,154 \| \| gis \| 100,254 \| \| askubuntu \| 267,135 \| \| diy \| 52,896 \| \| academia \| 32,137 \| \| blender \| 54,153 \| \| cs \| 30,010 \| \| chemistry \| 27,061 \| \| judaism \| 26,085 \| \| crypto \| 19,404 \| \| android \| 38,077 \| \| ja \| 17,376 \| \| christianity \| 11,498 \| \| graphicdesign \| 28,083 \| \| aviation \| 18,755 \| \| ethereum \| 26,124 \| \| biology \| 19,277 \| \| datascience \| 20,503 \| \| law \| 16,133 \| \| dsp \| 17,430 \| \| japanese \| 20,948 \| \| hermeneutics \| 9,516 \| \| bicycles \| 15,708 \| \| arduino \| 16,281 \| \| history \| 10,766 \| \| bitcoin \| 22,474 \| \| cooking \| 22,641 \| \| hinduism \| 8,999 \| \| codegolf \| 8,211 \| \| boardgames \| 11,805 \| \| emacs \| 16,830 \| \| economics \| 8,844 \| \| gardening \| 13,246 \| \| astronomy \| 9,086 \| \| islam \| 10,052 \| \| german \| 13,733 \| \| fitness \| 8,297 \| \| french \| 10,578 \| \| anime \| 10,131 \| \| craftcms \| 11,236 \| \| cstheory \| 7,742 \| \| engineering \| 8,649 \| \| buddhism \| 6,787 \| \| linguistics \| 6,843 \| \| ai \| 5,763 \| \| expressionengine \| 10,742 \| \| cogsci \| 5,101 \| \| chinese \| 8,646 \| \| chess \| 6,392 \| \| civicrm \| 10,648 \| \| literature \| 3,539 \| \| interpersonal \| 3,398 \| \| health \| 4,494 \| \| avp \| 6,450 \| \| earthscience \| 4,396 \| \| joomla \| 5,887 \| \| homebrew \| 5,608 \| \| expatriates \| 4,913 \| \| latin \| 3,969 \| \| matheducators \| 2,706 \| \| ham \| 3,501 \| \| genealogy \| 2,895 \| \| 3dprinting \| 3,488 \| \| elementaryos \| 5,917 \| \| bioinformatics \| 3,135 \| \| devops \| 3,462 \| \| hsm \| 2,517 \| \| italian \| 3,101 \| \| computergraphics \| 2,306 \| \| martialarts \| 1,737 \| \| bricks \| 3,530 \| \| freelancing \| 1,663 \| \| crafts \| 1,659 \| \| lifehacks \| 2,576 \| \| cseducators \| 902 \| \| materials \| 1,101 \| \| hardwarerecs \| 2,050 \| \| iot \| 1,359 \| \| eosio \| 1,940 \| \| languagelearning \| 948 \| \| korean \| 1,406 \| \| coffee \| 1,188 \| \| esperanto \| 1,466 \| \| beer \| 1,012 \| \| ebooks \| 1,107 \| \| iota \| 775 \| \| cardano \| 248 \| \| drones \| 496 \| \| conlang \| 334 \| \| pt \| 103,277 \| \| stats \| 115,679 \| \| unix \| 155,414 \| \| physics \| 141,230 \| \| tex \| 171,628 \| \| serverfault \| 238,507 \| \| salesforce \| 87,272 \| \| wordpress \| 83,621 \| \| softwareengineering \| 51,326 \| \| scifi \| 54,805 \| \| security \| 51,355 \| \| ru \| 253,289 \| \| superuser \| 352,610 \| \| sharepoint \| 80,420 \| \| rpg \| 40,435 \| \| travel \| 36,533 \| \| worldbuilding \| 26,210 \| \| meta \| 1,000 \| \| workplace \| 24,012 \| \| ux \| 28,901 \| \| money \| 29,404 \| \| webmasters \| 30,370 \| \| raspberrypi \| 24,143 \| \| photo \| 23,204 \| \| music \| 19,936 \| \| philosophy \| 13,114 \| \| puzzling \| 17,448 \| \| movies \| 18,243 \| \| quant \| 12,933 \| \| politics \| 11,047 \| \| space \| 12,893 \| \| mechanics \| 18,613 \| \| skeptics \| 8,145 \| \| rus \| 16,528 \| \| writers \| 9,867 \| \| webapps \| 24,867 \| \| softwarerecs \| 11,761 \| \| networkengineering \| 12,590 \| \| parenting \| 5,998 \| \| scicomp \| 7,036 \| \| sqa \| 9,256 \| \| sitecore \| 7,838 \| \| vi \| 9,000 \| \| spanish \| 7,675 \| \| pm \| 5,435 \| \| pets \| 6,156 \| \| sound \| 8,303 \| \| reverseengineering \| 5,817 \| \| outdoors \| 5,278 \| \| tridion \| 5,907 \| \| retrocomputing \| 3,907 \| \| robotics \| 4,648 \| \| quantumcomputing \| 4,320 \| \| sports \| 4,707 \| \| russian \| 3,937 \| \| opensource \| 3,221 \| \| woodworking \| 2,955 \| \| patents \| 3,573 \| \| tor \| 4,167 \| \| ukrainian \| 1,767 \| \| opendata \| 3,842 \| \| monero \| 3,508 \| \| sustainability \| 1,674 \| \| portuguese \| 1,964 \| \| mythology \| 1,595 \| \| musicfans \| 2,431 \| \| or \| 1,490 \| \| poker \| 1,665 \| \| windowsphone \| 2,807 \| \| moderators \| 504 \| \| stackapps \| 1,518 \| \| stellar \| 1,078 \| \| vegetarianism \| 585 \| \| tezos \| 1,169 \| \| total \| 4,750,619 \| ## Dataset Creation ### Curation Rationale We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically. ### Source Data The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange) #### Initial Data Collection and Normalization We collected the data from the math community. We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters. When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers. #### Who are the source language producers? Questions and answers are written by the community developpers of Stack Exchange. ## Additional Information ### Licensing Information Please see the license information at: https://archive.org/details/stackexchange ### Citation Information ``` @misc{StackExchangeDataset, author = {Flax Sentence Embeddings Team}, title = {Stack Exchange question pairs}, year = {2021}, howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/}, } ``` ### Contributions Thanks to the Flax Sentence Embeddings team for adding this dataset.
allenai/multinews_dense_max	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual pretty_name: Multi-News size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: multi-news train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: document: text summary: target metrics: - type: rouge name: Rouge --- This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __dense__ retriever. The retrieval pipeline used: - __query__: The `summary` field of each example - __corpus__: The union of all documents in the `train`, `validation` and `test` splits - __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings - __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==10` Retrieval results on the `train` set: Recall@100 \| Rprec \| Precision@k \| Recall@k \| \| ----------- \| ----------- \| ----------- \| ----------- \| \| 0.8661 \| 0.6867 \| 0.2118 \| 0.7966 \| Retrieval results on the `validation` set: Recall@100 \| Rprec \| Precision@k \| Recall@k \| \| ----------- \| ----------- \| ----------- \| ----------- \| \| 0.8626 \| 0.6859 \| 0.2083 \| 0.7949 \| Retrieval results on the `test` set: Recall@100 \| Rprec \| Precision@k \| Recall@k \| \| ----------- \| ----------- \| ----------- \| ----------- \| \| 0.8625 \| 0.6927 \| 0.2096 \| 0.7971 \|
distilled-one-sec-cv12-each-chunk-uniq/chunk_68	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1114218784.0 num_examples: 217112 download_size: 1136538595 dataset_size: 1114218784.0 --- # Dataset Card for "chunk_68" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_HWERI__pythia-70m-deduped-cleansharegpt	--- pretty_name: Evaluation run of HWERI/pythia-70m-deduped-cleansharegpt dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [HWERI/pythia-70m-deduped-cleansharegpt](https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt)\ \ 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_HWERI__pythia-70m-deduped-cleansharegpt\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T05:46:53.221413](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__pythia-70m-deduped-cleansharegpt/blob/main/results_2023-10-29T05-46-53.221413.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\": 0.0,\n \"\ em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\ acc\": 0.2600631412786109,\n \"acc_stderr\": 0.007020548332172166\n },\n\ \ \"harness\|drop\|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\ \ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness\|gsm8k\|5\"\ : {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5201262825572218,\n \"acc_stderr\": 0.014041096664344332\n\ \ }\n}\n```" repo_url: https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|arc:challenge\|25_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-13T12-28-53.949092.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T05_46_53.221413 path: - '/details_harness\|drop\|3_2023-10-29T05-46-53.221413.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-29T05-46-53.221413.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T05_46_53.221413 path: - '/details_harness\|gsm8k\|5_2023-10-29T05-46-53.221413.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-29T05-46-53.221413.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hellaswag\|10_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T12-28-53.949092.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_13T12_28_53.949092 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-13T12-28-53.949092.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T05_46_53.221413 path: - '/details_harness\|winogrande\|5_2023-10-29T05-46-53.221413.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-29T05-46-53.221413.parquet' - config_name: results data_files: - split: 2023_09_13T12_28_53.949092 path: - results_2023-09-13T12-28-53.949092.parquet - split: 2023_10_29T05_46_53.221413 path: - results_2023-10-29T05-46-53.221413.parquet - split: latest path: - results_2023-10-29T05-46-53.221413.parquet --- # Dataset Card for Evaluation run of HWERI/pythia-70m-deduped-cleansharegpt ## Dataset Description - Homepage: - Repository: https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt - 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 [HWERI/pythia-70m-deduped-cleansharegpt](https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt) 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_HWERI__pythia-70m-deduped-cleansharegpt", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T05:46:53.221413](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__pythia-70m-deduped-cleansharegpt/blob/main/results_2023-10-29T05-46-53.221413.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": 0.0, "em_stderr": 0.0, "f1": 0.0, "f1_stderr": 0.0, "acc": 0.2600631412786109, "acc_stderr": 0.007020548332172166 }, "harness\|drop\|3": { "em": 0.0, "em_stderr": 0.0, "f1": 0.0, "f1_stderr": 0.0 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.5201262825572218, "acc_stderr": 0.014041096664344332 } } ``` ### 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]
miehin/test13aug	--- license: openrail ---
arthurmluz/GPTextSum_data-wiki_gptextsum_results	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 splits: - name: validation num_bytes: 28733 num_examples: 20 download_size: 35161 dataset_size: 28733 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "GPTextSum_data-wiki_gptextsum_results" rouge= {'rouge1': 0.47135937656590865, 'rouge2': 0.25940674061381325, 'rougeL': 0.38504581702889945, 'rougeLsum': 0.38504581702889945} bert= {'precision': 0.788231149315834, 'recall': 0.7805202662944793, 'f1': 0.7840157747268677}
lhallee/abstract_domain_copd	--- dataset_info: features: - name: a dtype: string - name: b dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 477301584 num_examples: 132453 - name: valid num_bytes: 9596971 num_examples: 2676 - name: test num_bytes: 4758204 num_examples: 1294 download_size: 200765538 dataset_size: 491656759 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* --- This dataset contains the cocitation abstracts related to COPD in the paper [Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings](arxiv.org/abs/2401.15713)
AdapterOcean/physics_dataset_standardized_cluster_4_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 7334043 num_examples: 3437 download_size: 0 dataset_size: 7334043 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "physics_dataset_standardized_cluster_4_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pourmand1376/alpaca-fa-multi	--- dataset_info: features: - name: instruction dtype: string - name: conversation dtype: string - name: SOURCE dtype: string splits: - name: train num_bytes: 2694606 num_examples: 3907 download_size: 1227811 dataset_size: 2694606 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - question-answering - text-generation - conversational language: - fa pretty_name: Alpaca Farsi Multi-turn size_categories: - 10K<n<100K --- # Dataset Card for "alpaca-fa-multi" This dataset is first published [here](https://www.kaggle.com/datasets/amirpourmand/alpaca-farsi) and then converted to this style to comply with open-assistant standards. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DataHammer/emotional_dialog	--- license: apache-2.0 task_categories: - text-generation - conversational language: - zh size_categories: - 1K<n<10K --- # Scientific Emotional Dialogue ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This is a dataset for emotional multi-turn dialogue on scientific research personnels. It consists of 1069 dialogues with 2709 turns. The Dialogue was first written by NLP practitioners and then expanded by GPT4. ### Supported Tasks and Leaderboards - Emotional Dialogue: The dataset can be used to instruction tuning for emotional dialogue. ### Languages Chinese ## Dataset Structure ### Data Instances A typical instance in the dataset: ``` [ "Human: 真是郁闷，我的论文被拒了，我觉得自己完全失败了。", "Bot: 嘿，别这么想嘛，论文被拒并不代表你是个失败者。审稿人的意见只是一种反馈，你可以从中学到很多东西。别放弃，修改论文并尝试其他期刊，我相信你能成功的！", "Human: 嗯，或许你说得对。但我还是觉得很失望，觉得自己好像没进步。", "Bot: 失望是正常的情绪，但不要让它打击你。论文写作是一个成长的过程，每次经历都是宝贵的。相信自己的能力，继续努力，你一定能取得进步的！", "Human: 谢谢你的鼓励，我会再试一次的。", "Bot: 太棒了！相信自己，你可以做到的！" ] ```
gracefulibk/yorubamt	--- license: afl-3.0 ---
Asap7772/skewlognormal_maxlength	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: output dtype: string - name: text dtype: string - name: alpaca_text dtype: string - name: prompt dtype: string - name: alpaca_prompt dtype: string - name: y_ref dtype: string - name: y_1 dtype: string - name: y_2 dtype: string - name: y_w dtype: string - name: y_w_alpaca dtype: string - name: y_l dtype: string - name: y_l_alpaca dtype: string - name: y_w_score dtype: float64 - name: y_l_score dtype: float64 - name: score_diff dtype: float64 splits: - name: train num_bytes: 77844991 num_examples: 19000 - name: test num_bytes: 4082779 num_examples: 1000 download_size: 40225253 dataset_size: 81927770 --- # Dataset Card for "skewlognormal_maxlength" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sxdave/emotion_detection	--- task_categories: - image-classification language: - en tags: - happy - sad - neutral size_categories: - n<1K ---
mizunorlk/mizuno	--- license: openrail ---
xaviviro/oasst1_ca	--- dataset_info: features: - name: message_id dtype: string - name: parent_id dtype: string - name: user_id dtype: string - name: created_date dtype: string - name: text dtype: string - name: role dtype: string - name: lang dtype: string - name: review_count dtype: int64 - name: review_result dtype: bool - name: deleted dtype: bool - name: rank dtype: float64 - name: synthetic dtype: bool - name: model_name dtype: 'null' - name: detoxify struct: - name: identity_attack dtype: float64 - name: insult dtype: float64 - name: obscene dtype: float64 - name: severe_toxicity dtype: float64 - name: sexual_explicit dtype: float64 - name: threat dtype: float64 - name: toxicity dtype: float64 - name: message_tree_id dtype: string - name: tree_state dtype: string - name: emojis struct: - name: count sequence: int64 - name: name sequence: string - name: labels struct: - name: count sequence: int64 - name: name sequence: string - name: value sequence: float64 splits: - name: validation num_bytes: 4690639 num_examples: 4399 - name: train num_bytes: 89877756 num_examples: 84398 download_size: 33007102 dataset_size: 94568395 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* language: - ca ---
open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test	--- pretty_name: Evaluation run of lu-vae/llama2-13b-sharegpt4-test dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lu-vae/llama2-13b-sharegpt4-test](https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test)\ \ 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_lu-vae__llama2-13b-sharegpt4-test\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-25T11:44:44.255542](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test/blob/main/results_2023-10-25T11-44-44.255542.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\": 0.002202181208053691,\n\ \ \"em_stderr\": 0.00048005108166192744,\n \"f1\": 0.06610843120805385,\n\ \ \"f1_stderr\": 0.0014639889801036593,\n \"acc\": 0.44600618846762125,\n\ \ \"acc_stderr\": 0.01064352054021588\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.00048005108166192744,\n\ \ \"f1\": 0.06610843120805385,\n \"f1_stderr\": 0.0014639889801036593\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.13115996967399546,\n \ \ \"acc_stderr\": 0.00929849923558786\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.760852407261247,\n \"acc_stderr\": 0.011988541844843902\n\ \ }\n}\n```" repo_url: https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|arc:challenge\|25_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-12T16-41-26.998548.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_25T11_44_44.255542 path: - '/details_harness\|drop\|3_2023-10-25T11-44-44.255542.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-25T11-44-44.255542.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_25T11_44_44.255542 path: - '/details_harness\|gsm8k\|5_2023-10-25T11-44-44.255542.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-25T11-44-44.255542.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hellaswag\|10_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T16-41-26.998548.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T16_41_26.998548 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-12T16-41-26.998548.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_25T11_44_44.255542 path: - '/details_harness\|winogrande\|5_2023-10-25T11-44-44.255542.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-25T11-44-44.255542.parquet' - config_name: results data_files: - split: 2023_09_12T16_41_26.998548 path: - results_2023-09-12T16-41-26.998548.parquet - split: 2023_10_25T11_44_44.255542 path: - results_2023-10-25T11-44-44.255542.parquet - split: latest path: - results_2023-10-25T11-44-44.255542.parquet --- # Dataset Card for Evaluation run of lu-vae/llama2-13b-sharegpt4-test ## Dataset Description - Homepage: - Repository: https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test - 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 [lu-vae/llama2-13b-sharegpt4-test](https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test) 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_lu-vae__llama2-13b-sharegpt4-test", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-25T11:44:44.255542](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test/blob/main/results_2023-10-25T11-44-44.255542.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": 0.002202181208053691, "em_stderr": 0.00048005108166192744, "f1": 0.06610843120805385, "f1_stderr": 0.0014639889801036593, "acc": 0.44600618846762125, "acc_stderr": 0.01064352054021588 }, "harness\|drop\|3": { "em": 0.002202181208053691, "em_stderr": 0.00048005108166192744, "f1": 0.06610843120805385, "f1_stderr": 0.0014639889801036593 }, "harness\|gsm8k\|5": { "acc": 0.13115996967399546, "acc_stderr": 0.00929849923558786 }, "harness\|winogrande\|5": { "acc": 0.760852407261247, "acc_stderr": 0.011988541844843902 } } ``` ### 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]
ravithejads/alpaca_urdu_cleaned_input	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: telugu_instruction dtype: string - name: telugu_input dtype: string - name: telugu_output dtype: string - name: telugu_transliterated_instruction dtype: string - name: telugu_transliterated_input dtype: string - name: telugu_transliterated_output dtype: string - name: urdu_instruction dtype: string - name: urdu_input dtype: string splits: - name: train num_bytes: 148976621 num_examples: 28910 download_size: 67681432 dataset_size: 148976621 configs: - config_name: default data_files: - split: train path: data/train-* ---
dhuynh95/Magicoder-Evol-Instruct-110K-Filtered_0.35-text	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 10765290 num_examples: 5000 download_size: 5317750 dataset_size: 10765290 configs: - config_name: default data_files: - split: train path: data/train-* ---
AlekseyKorshuk/intothatgoodnight-guanaco-chatml	--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: do_train dtype: bool - name: role dtype: string splits: - name: train num_bytes: 69125408 num_examples: 50000 download_size: 38037513 dataset_size: 69125408 --- # Dataset Card for "intothatgoodnight-guanaco-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/squad_no_rare_v4_train_30_eval_10_recite_ans_sent	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 581992 num_examples: 368 - name: validation num_bytes: 48145 num_examples: 50 download_size: 112955 dataset_size: 630137 --- # Dataset Card for "squad_no_rare_v4_train_30_eval_10_recite_ans_sent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
OpenPipe/hacker-news	--- dataset_info: features: - name: id dtype: int64 - name: type dtype: string - name: by dtype: string - name: time dtype: timestamp[us] - name: title dtype: string - name: text dtype: string - name: url dtype: string - name: score dtype: float64 - name: parent dtype: float64 - name: top_level_parent dtype: int64 - name: descendants dtype: float64 - name: kids sequence: int64 - name: deleted dtype: bool - name: dead dtype: bool splits: - name: train num_bytes: 16886975696 num_examples: 38109500 download_size: 9948795138 dataset_size: 16886975696 configs: - config_name: default data_files: - split: train path: data/train-* --- # Hacker News posts and comments This is a dataset of all HN posts and comments, current as of November 1, 2023.
hamza50/testimg	--- license: wtfpl ---
bagusatmaja/securityobjectives	--- language: - en ---
zz545906747/blue_eye_dragon	--- license: apache-2.0 ---
open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3	--- pretty_name: Evaluation run of shaohang/Sparse0.5_OPT-1.3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [shaohang/Sparse0.5_OPT-1.3](https://huggingface.co/shaohang/Sparse0.5_OPT-1.3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-16T21:48:19.303713](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3/blob/main/results_2023-09-16T21-48-19.303713.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\": 0.003145973154362416,\n\ \ \"em_stderr\": 0.0005734993648436398,\n \"f1\": 0.047173867449664536,\n\ \ \"f1_stderr\": 0.0012666649528854216,\n \"acc\": 0.29319675461487227,\n\ \ \"acc_stderr\": 0.007301498172995543\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436398,\n\ \ \"f1\": 0.047173867449664536,\n \"f1_stderr\": 0.0012666649528854216\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.000758150113722517,\n \ \ \"acc_stderr\": 0.0007581501137225237\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.585635359116022,\n \"acc_stderr\": 0.013844846232268563\n\ \ }\n}\n```" repo_url: https://huggingface.co/shaohang/Sparse0.5_OPT-1.3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_09_16T21_48_19.303713 path: - '/details_harness\|drop\|3_2023-09-16T21-48-19.303713.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-16T21-48-19.303713.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_16T21_48_19.303713 path: - '/details_harness\|gsm8k\|5_2023-09-16T21-48-19.303713.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-16T21-48-19.303713.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_16T21_48_19.303713 path: - '/details_harness\|winogrande\|5_2023-09-16T21-48-19.303713.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-16T21-48-19.303713.parquet' - config_name: results data_files: - split: 2023_09_16T21_48_19.303713 path: - results_2023-09-16T21-48-19.303713.parquet - split: latest path: - results_2023-09-16T21-48-19.303713.parquet --- # Dataset Card for Evaluation run of shaohang/Sparse0.5_OPT-1.3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/shaohang/Sparse0.5_OPT-1.3 - 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 [shaohang/Sparse0.5_OPT-1.3](https://huggingface.co/shaohang/Sparse0.5_OPT-1.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-16T21:48:19.303713](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3/blob/main/results_2023-09-16T21-48-19.303713.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": 0.003145973154362416, "em_stderr": 0.0005734993648436398, "f1": 0.047173867449664536, "f1_stderr": 0.0012666649528854216, "acc": 0.29319675461487227, "acc_stderr": 0.007301498172995543 }, "harness\|drop\|3": { "em": 0.003145973154362416, "em_stderr": 0.0005734993648436398, "f1": 0.047173867449664536, "f1_stderr": 0.0012666649528854216 }, "harness\|gsm8k\|5": { "acc": 0.000758150113722517, "acc_stderr": 0.0007581501137225237 }, "harness\|winogrande\|5": { "acc": 0.585635359116022, "acc_stderr": 0.013844846232268563 } } ``` ### 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]
aashay96/translated-dataset-synthetic-retrieval-tasks	--- license: apache-2.0 ---
open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1	--- pretty_name: Evaluation run of mncai/mistral-7b-dpo-merge-v1.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mncai/mistral-7b-dpo-merge-v1.1](https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_mncai__mistral-7b-dpo-merge-v1.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-18T08:24:07.649575](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1/blob/main/results_2023-12-18T08-24-07.649575.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\": 0.653910016295201,\n\ \ \"acc_stderr\": 0.03202751255847507,\n \"acc_norm\": 0.6535699044300431,\n\ \ \"acc_norm_stderr\": 0.03269009606304505,\n \"mc1\": 0.5520195838433293,\n\ \ \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6847598329649042,\n\ \ \"mc2_stderr\": 0.015228501222479747\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6945392491467577,\n \"acc_stderr\": 0.013460080478002503,\n\ \ \"acc_norm\": 0.7252559726962458,\n \"acc_norm_stderr\": 0.013044617212771227\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7080262895837482,\n\ \ \"acc_stderr\": 0.004537410615572944,\n \"acc_norm\": 0.8814977096195977,\n\ \ \"acc_norm_stderr\": 0.0032254141192897138\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724053,\n\ \ \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724053\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\"\ : {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\ \ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.030956636328566545,\n\ \ \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.030956636328566545\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.034076320938540516,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.034076320938540516\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\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\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371802\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069356,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069356\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4770949720670391,\n\ \ \"acc_stderr\": 0.016704945740326188,\n \"acc_norm\": 0.4770949720670391,\n\ \ \"acc_norm_stderr\": 0.016704945740326188\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\ \ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4716312056737589,\n \"acc_stderr\": 0.02977945095730307,\n \ \ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.02977945095730307\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4706649282920469,\n\ \ \"acc_stderr\": 0.012748238397365549,\n \"acc_norm\": 0.4706649282920469,\n\ \ \"acc_norm_stderr\": 0.012748238397365549\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n\ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724553,\n \ \ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724553\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\": 0.7346938775510204,\n \"acc_stderr\": 0.02826388994378459,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.02826388994378459\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5520195838433293,\n\ \ \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6847598329649042,\n\ \ \"mc2_stderr\": 0.015228501222479747\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8232044198895028,\n \"acc_stderr\": 0.010721923287918753\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7088703563305534,\n \ \ \"acc_stderr\": 0.012513215297888463\n }\n}\n```" repo_url: https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1 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: 2023_12_18T08_24_07.649575 path: - '/details_harness\|arc:challenge\|25_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-18T08-24-07.649575.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|gsm8k\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hellaswag\|10_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-24-07.649575.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-18T08-24-07.649575.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '/details_harness\|winogrande\|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-18T08-24-07.649575.parquet' - config_name: results data_files: - split: 2023_12_18T08_24_07.649575 path: - results_2023-12-18T08-24-07.649575.parquet - split: latest path: - results_2023-12-18T08-24-07.649575.parquet --- # Dataset Card for Evaluation run of mncai/mistral-7b-dpo-merge-v1.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mncai/mistral-7b-dpo-merge-v1.1](https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_mncai__mistral-7b-dpo-merge-v1.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:24:07.649575](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1/blob/main/results_2023-12-18T08-24-07.649575.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": 0.653910016295201, "acc_stderr": 0.03202751255847507, "acc_norm": 0.6535699044300431, "acc_norm_stderr": 0.03269009606304505, "mc1": 0.5520195838433293, "mc1_stderr": 0.017408513063422917, "mc2": 0.6847598329649042, "mc2_stderr": 0.015228501222479747 }, "harness\|arc:challenge\|25": { "acc": 0.6945392491467577, "acc_stderr": 0.013460080478002503, "acc_norm": 0.7252559726962458, "acc_norm_stderr": 0.013044617212771227 }, "harness\|hellaswag\|10": { "acc": 0.7080262895837482, "acc_stderr": 0.004537410615572944, "acc_norm": 0.8814977096195977, "acc_norm_stderr": 0.0032254141192897138 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.037385206761196686, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.037385206761196686 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7245283018867924, "acc_stderr": 0.027495663683724053, "acc_norm": 0.7245283018867924, "acc_norm_stderr": 0.027495663683724053 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6763005780346821, "acc_stderr": 0.035676037996391706, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.035676037996391706 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406783, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406783 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.029376616484945633, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.029376616484945633 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6512605042016807, "acc_stderr": 0.030956636328566545, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.030956636328566545 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.034076320938540516, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.034076320938540516 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "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": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "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": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371802, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371802 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069356, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069356 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4770949720670391, "acc_stderr": 0.016704945740326188, "acc_norm": 0.4770949720670391, "acc_norm_stderr": 0.016704945740326188 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7376543209876543, "acc_stderr": 0.024477222856135114, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.024477222856135114 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4716312056737589, "acc_stderr": 0.02977945095730307, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.02977945095730307 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4706649282920469, "acc_stderr": 0.012748238397365549, "acc_norm": 0.4706649282920469, "acc_norm_stderr": 0.012748238397365549 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.02806499816704009, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.02806499816704009 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6699346405228758, "acc_stderr": 0.019023726160724553, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.019023726160724553 }, "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": 0.7346938775510204, "acc_stderr": 0.02826388994378459, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.02826388994378459 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5520195838433293, "mc1_stderr": 0.017408513063422917, "mc2": 0.6847598329649042, "mc2_stderr": 0.015228501222479747 }, "harness\|winogrande\|5": { "acc": 0.8232044198895028, "acc_stderr": 0.010721923287918753 }, "harness\|gsm8k\|5": { "acc": 0.7088703563305534, "acc_stderr": 0.012513215297888463 } } ``` ## 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]
maxidl/MathInstruct-de	--- dataset_info: features: - name: source dtype: string - name: messages_en list: - name: content dtype: string - name: role dtype: string - name: messages_de list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 87671574 num_examples: 56793 download_size: 43803287 dataset_size: 87671574 configs: - config_name: default data_files: - split: train path: data/train-* --- German version of [TIGER-Lab/MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct). Translated using DeepL (informal style). Currently limited to the first 56_793 examples. \|lang\|#chars\| \|---\|---\| \|en\|38_793_154\| \|de\|42_851_569\|
kannanwisen/Indian-Traffic-Sign-Classification	--- license: cc-by-4.0 ---
ghomasHudson/muld_NarrativeQA	--- dataset_info: features: - name: input dtype: string - name: output sequence: string splits: - name: test num_bytes: 3435452065 num_examples: 10143 - name: train num_bytes: 11253796383 num_examples: 32747 - name: validation num_bytes: 1176625993 num_examples: 3373 download_size: 8819172017 dataset_size: 15865874441 --- # Dataset Card for "muld_NarrativeQA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alirzb/WrongPreds	--- dataset_info: features: - name: array sequence: float64 - name: label_true dtype: int64 - name: label_pred dtype: int64 - name: id dtype: string - name: ws dtype: image splits: - name: train num_bytes: 4330982.0 num_examples: 9 download_size: 1745905 dataset_size: 4330982.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
nielsr/datacomp_small_with_language	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: uid dtype: string - name: url dtype: string - name: text dtype: string - name: original_width dtype: int64 - name: original_height dtype: int64 - name: clip_b32_similarity_score dtype: float32 - name: clip_l14_similarity_score dtype: float32 - name: face_bboxes sequence: sequence: float64 - name: sha256 dtype: string - name: detected_language dtype: string splits: - name: train num_bytes: 4108589578 num_examples: 12800000 download_size: 3339674305 dataset_size: 4108589578 --- # Dataset Card for "datacomp_small_with_language" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pixparse/docvqa-wds	--- license: mit --- # Dataset Card for DocVQA Dataset ## Dataset Description - Point of Contact from curators: [Minesh Mathew](mailto:minesh.mathew@gmail.com), [Dimosthenis Karatzas](dimos@cvc.uab.es), [C. V. Jawahar](jawahar@iiit.ac.in) - Point of Contact Hugging Face: [Pablo Montalvo](mailto:pablo@huggingface.co) ### Dataset Summary DocVQA dataset is a document dataset introduced in Mathew et al. (2021) consisting of 50,000 questions defined on 12,000+ document images. This is a webdataset (.tar shard) version equivalent to https://huggingface.co/datasets/pixparse/docvqa-single-page-questions in content. Please visit the challenge page (https://rrc.cvc.uab.es/?ch=17) and paper (https://arxiv.org/abs/2007.00398) for further information. ### Usage This dataset can be used with current releases of Hugging Face `datasets` or `chug` libraries. Examples WIP ### Data Splits #### Train * 10194 images, 39463 questions and answers. ### Validation * 1286 images, 5349 questions and answers. ### Test * 1,287 images, 5,188 questions. ## Additional Information ### Dataset Curators For original authors of the dataset, see citation below. Hugging Face points of contact for this instance: Pablo Montalvo, Ross Wightman ### Licensing Information MIT ### Citation Information ```bibtex @InProceedings{docvqa_wacv, author = {Mathew, Minesh and Karatzas, Dimosthenis and Jawahar, C.V.}, title = {DocVQA: A Dataset for VQA on Document Images}, booktitle = {WACV}, year = {2021}, pages = {2200-2209} } ```
sankovic/jotapedataset	--- license: openrail ---
AlekseyKorshuk/product-photography-v1-categorized	--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: category dtype: string - name: product dtype: string - name: prompt dtype: string - name: image_mask dtype: image - name: text dtype: string splits: - name: train num_bytes: 6054931158.644 num_examples: 3606 - name: validation num_bytes: 289483345.0 num_examples: 176 download_size: 6341502964 dataset_size: 6344414503.644 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp	--- pretty_name: Evaluation run of allknowingroger/Synthiallamaguanco-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [allknowingroger/Synthiallamaguanco-7B-slerp](https://huggingface.co/allknowingroger/Synthiallamaguanco-7B-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-10T20:19:16.142131](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp/blob/main/results_2024-04-10T20-19-16.142131.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\": 0.5022519883105377,\n\ \ \"acc_stderr\": 0.03435165246376145,\n \"acc_norm\": 0.5073979301587215,\n\ \ \"acc_norm_stderr\": 0.03511097439217402,\n \"mc1\": 0.29865361077111385,\n\ \ \"mc1_stderr\": 0.016021570613768542,\n \"mc2\": 0.455854048100616,\n\ \ \"mc2_stderr\": 0.01483939681310818\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5102389078498294,\n \"acc_stderr\": 0.014608326906285012,\n\ \ \"acc_norm\": 0.5435153583617748,\n \"acc_norm_stderr\": 0.01455594976049644\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5936068512248556,\n\ \ \"acc_stderr\": 0.004901558132335522,\n \"acc_norm\": 0.7909778928500298,\n\ \ \"acc_norm_stderr\": 0.00405779217189357\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.45925925925925926,\n\ \ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.45925925925925926,\n\ \ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.48026315789473684,\n \"acc_stderr\": 0.040657710025626036,\n\ \ \"acc_norm\": 0.48026315789473684,\n \"acc_norm_stderr\": 0.040657710025626036\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5433962264150943,\n \"acc_stderr\": 0.03065674869673943,\n\ \ \"acc_norm\": 0.5433962264150943,\n \"acc_norm_stderr\": 0.03065674869673943\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5138888888888888,\n\ \ \"acc_stderr\": 0.041795966175810016,\n \"acc_norm\": 0.5138888888888888,\n\ \ \"acc_norm_stderr\": 0.041795966175810016\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.41040462427745666,\n\ \ \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.41040462427745666,\n\ \ \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.03257901482099835,\n\ \ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.03257901482099835\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.30701754385964913,\n\ \ \"acc_stderr\": 0.04339138322579862,\n \"acc_norm\": 0.30701754385964913,\n\ \ \"acc_norm_stderr\": 0.04339138322579862\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.30158730158730157,\n \"acc_stderr\": 0.023636975996101806,\n \"\ acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.023636975996101806\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\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\": 0.535483870967742,\n\ \ \"acc_stderr\": 0.028372287797962935,\n \"acc_norm\": 0.535483870967742,\n\ \ \"acc_norm_stderr\": 0.028372287797962935\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.3793103448275862,\n \"acc_stderr\": 0.03413963805906235,\n\ \ \"acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.03413963805906235\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\"\ : 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6484848484848484,\n \"acc_stderr\": 0.037282069986826503,\n\ \ \"acc_norm\": 0.6484848484848484,\n \"acc_norm_stderr\": 0.037282069986826503\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6212121212121212,\n \"acc_stderr\": 0.03456088731993747,\n \"\ acc_norm\": 0.6212121212121212,\n \"acc_norm_stderr\": 0.03456088731993747\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7305699481865285,\n \"acc_stderr\": 0.03201867122877794,\n\ \ \"acc_norm\": 0.7305699481865285,\n \"acc_norm_stderr\": 0.03201867122877794\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.47435897435897434,\n \"acc_stderr\": 0.025317649726448652,\n\ \ \"acc_norm\": 0.47435897435897434,\n \"acc_norm_stderr\": 0.025317649726448652\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073845,\n \ \ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073845\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.0322529423239964,\n \ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.0322529423239964\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.6844036697247706,\n \"acc_stderr\": 0.01992611751386967,\n \"\ acc_norm\": 0.6844036697247706,\n \"acc_norm_stderr\": 0.01992611751386967\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.3333333333333333,\n \"acc_stderr\": 0.03214952147802749,\n \"\ acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.03214952147802749\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.6862745098039216,\n \"acc_stderr\": 0.03256685484460388,\n \"\ acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.03256685484460388\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7341772151898734,\n \"acc_stderr\": 0.028756799629658342,\n \ \ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6188340807174888,\n\ \ \"acc_stderr\": 0.03259625118416828,\n \"acc_norm\": 0.6188340807174888,\n\ \ \"acc_norm_stderr\": 0.03259625118416828\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\ \ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6033057851239669,\n \"acc_stderr\": 0.044658697805310094,\n \"\ acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.044658697805310094\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5648148148148148,\n\ \ \"acc_stderr\": 0.04792898170907061,\n \"acc_norm\": 0.5648148148148148,\n\ \ \"acc_norm_stderr\": 0.04792898170907061\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.5460122699386503,\n \"acc_stderr\": 0.0391170190467718,\n\ \ \"acc_norm\": 0.5460122699386503,\n \"acc_norm_stderr\": 0.0391170190467718\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4017857142857143,\n\ \ \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.4017857142857143,\n\ \ \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7735042735042735,\n\ \ \"acc_stderr\": 0.027421007295392923,\n \"acc_norm\": 0.7735042735042735,\n\ \ \"acc_norm_stderr\": 0.027421007295392923\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.6973180076628352,\n\ \ \"acc_stderr\": 0.016428781581749364,\n \"acc_norm\": 0.6973180076628352,\n\ \ \"acc_norm_stderr\": 0.016428781581749364\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5346820809248555,\n \"acc_stderr\": 0.026854257928258875,\n\ \ \"acc_norm\": 0.5346820809248555,\n \"acc_norm_stderr\": 0.026854257928258875\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767867,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767867\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.545751633986928,\n \"acc_stderr\": 0.02850980780262659,\n\ \ \"acc_norm\": 0.545751633986928,\n \"acc_norm_stderr\": 0.02850980780262659\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6109324758842444,\n\ \ \"acc_stderr\": 0.027690337536485376,\n \"acc_norm\": 0.6109324758842444,\n\ \ \"acc_norm_stderr\": 0.027690337536485376\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.558641975308642,\n \"acc_stderr\": 0.027628737155668777,\n\ \ \"acc_norm\": 0.558641975308642,\n \"acc_norm_stderr\": 0.027628737155668777\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3900709219858156,\n \"acc_stderr\": 0.02909767559946393,\n \ \ \"acc_norm\": 0.3900709219858156,\n \"acc_norm_stderr\": 0.02909767559946393\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3709256844850065,\n\ \ \"acc_stderr\": 0.012337391684530312,\n \"acc_norm\": 0.3709256844850065,\n\ \ \"acc_norm_stderr\": 0.012337391684530312\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5110294117647058,\n \"acc_stderr\": 0.030365446477275675,\n\ \ \"acc_norm\": 0.5110294117647058,\n \"acc_norm_stderr\": 0.030365446477275675\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.020227834851568375,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.020227834851568375\n },\n \"harness\|hendrycksTest-public_relations\|5\"\ : {\n \"acc\": 0.5545454545454546,\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\": 0.5673469387755102,\n\ \ \"acc_stderr\": 0.031717528240626645,\n \"acc_norm\": 0.5673469387755102,\n\ \ \"acc_norm_stderr\": 0.031717528240626645\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.6417910447761194,\n \"acc_stderr\": 0.03390393042268814,\n\ \ \"acc_norm\": 0.6417910447761194,\n \"acc_norm_stderr\": 0.03390393042268814\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\": 0.42168674698795183,\n \"acc_stderr\": 0.03844453181770917,\n\ \ \"acc_norm\": 0.42168674698795183,\n \"acc_norm_stderr\": 0.03844453181770917\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.6900584795321637,\n\ \ \"acc_stderr\": 0.035469769593931624,\n \"acc_norm\": 0.6900584795321637,\n\ \ \"acc_norm_stderr\": 0.035469769593931624\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 0.29865361077111385,\n \"mc1_stderr\": 0.016021570613768542,\n\ \ \"mc2\": 0.455854048100616,\n \"mc2_stderr\": 0.01483939681310818\n\ \ },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.744277821625888,\n\ \ \"acc_stderr\": 0.012261253845440473\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.18726307808946172,\n \"acc_stderr\": 0.010745914199510815\n\ \ }\n}\n```" repo_url: https://huggingface.co/allknowingroger/Synthiallamaguanco-7B-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|arc:challenge\|25_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-10T20-19-16.142131.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|gsm8k\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hellaswag\|10_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-10T20-19-16.142131.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-10T20-19-16.142131.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '/details_harness\|winogrande\|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-10T20-19-16.142131.parquet' - config_name: results data_files: - split: 2024_04_10T20_19_16.142131 path: - results_2024-04-10T20-19-16.142131.parquet - split: latest path: - results_2024-04-10T20-19-16.142131.parquet --- # Dataset Card for Evaluation run of allknowingroger/Synthiallamaguanco-7B-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [allknowingroger/Synthiallamaguanco-7B-slerp](https://huggingface.co/allknowingroger/Synthiallamaguanco-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-10T20:19:16.142131](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp/blob/main/results_2024-04-10T20-19-16.142131.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": 0.5022519883105377, "acc_stderr": 0.03435165246376145, "acc_norm": 0.5073979301587215, "acc_norm_stderr": 0.03511097439217402, "mc1": 0.29865361077111385, "mc1_stderr": 0.016021570613768542, "mc2": 0.455854048100616, "mc2_stderr": 0.01483939681310818 }, "harness\|arc:challenge\|25": { "acc": 0.5102389078498294, "acc_stderr": 0.014608326906285012, "acc_norm": 0.5435153583617748, "acc_norm_stderr": 0.01455594976049644 }, "harness\|hellaswag\|10": { "acc": 0.5936068512248556, "acc_stderr": 0.004901558132335522, "acc_norm": 0.7909778928500298, "acc_norm_stderr": 0.00405779217189357 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464242, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464242 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.48026315789473684, "acc_stderr": 0.040657710025626036, "acc_norm": 0.48026315789473684, "acc_norm_stderr": 0.040657710025626036 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5433962264150943, "acc_stderr": 0.03065674869673943, "acc_norm": 0.5433962264150943, "acc_norm_stderr": 0.03065674869673943 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5138888888888888, "acc_stderr": 0.041795966175810016, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.041795966175810016 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.41040462427745666, "acc_stderr": 0.03750757044895537, "acc_norm": 0.41040462427745666, "acc_norm_stderr": 0.03750757044895537 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.20588235294117646, "acc_stderr": 0.04023382273617747, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.04023382273617747 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4595744680851064, "acc_stderr": 0.03257901482099835, "acc_norm": 0.4595744680851064, "acc_norm_stderr": 0.03257901482099835 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.30701754385964913, "acc_stderr": 0.04339138322579862, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.04339138322579862 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.30158730158730157, "acc_stderr": 0.023636975996101806, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.023636975996101806 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "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": 0.535483870967742, "acc_stderr": 0.028372287797962935, "acc_norm": 0.535483870967742, "acc_norm_stderr": 0.028372287797962935 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3793103448275862, "acc_stderr": 0.03413963805906235, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.03413963805906235 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6484848484848484, "acc_stderr": 0.037282069986826503, "acc_norm": 0.6484848484848484, "acc_norm_stderr": 0.037282069986826503 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6212121212121212, "acc_stderr": 0.03456088731993747, "acc_norm": 0.6212121212121212, "acc_norm_stderr": 0.03456088731993747 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7305699481865285, "acc_stderr": 0.03201867122877794, "acc_norm": 0.7305699481865285, "acc_norm_stderr": 0.03201867122877794 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.47435897435897434, "acc_stderr": 0.025317649726448652, "acc_norm": 0.47435897435897434, "acc_norm_stderr": 0.025317649726448652 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.26666666666666666, "acc_stderr": 0.026962424325073845, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.026962424325073845 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.0322529423239964, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.0322529423239964 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6844036697247706, "acc_stderr": 0.01992611751386967, "acc_norm": 0.6844036697247706, "acc_norm_stderr": 0.01992611751386967 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.03214952147802749, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.03214952147802749 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6862745098039216, "acc_stderr": 0.03256685484460388, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.03256685484460388 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7341772151898734, "acc_stderr": 0.028756799629658342, "acc_norm": 0.7341772151898734, "acc_norm_stderr": 0.028756799629658342 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6188340807174888, "acc_stderr": 0.03259625118416828, "acc_norm": 0.6188340807174888, "acc_norm_stderr": 0.03259625118416828 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5954198473282443, "acc_stderr": 0.043046937953806645, "acc_norm": 0.5954198473282443, "acc_norm_stderr": 0.043046937953806645 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6033057851239669, "acc_stderr": 0.044658697805310094, "acc_norm": 0.6033057851239669, "acc_norm_stderr": 0.044658697805310094 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5648148148148148, "acc_stderr": 0.04792898170907061, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.04792898170907061 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5460122699386503, "acc_stderr": 0.0391170190467718, "acc_norm": 0.5460122699386503, "acc_norm_stderr": 0.0391170190467718 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4017857142857143, "acc_stderr": 0.04653333146973646, "acc_norm": 0.4017857142857143, "acc_norm_stderr": 0.04653333146973646 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6699029126213593, "acc_stderr": 0.0465614711001235, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.0465614711001235 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7735042735042735, "acc_stderr": 0.027421007295392923, "acc_norm": 0.7735042735042735, "acc_norm_stderr": 0.027421007295392923 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6973180076628352, "acc_stderr": 0.016428781581749364, "acc_norm": 0.6973180076628352, "acc_norm_stderr": 0.016428781581749364 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5346820809248555, "acc_stderr": 0.026854257928258875, "acc_norm": 0.5346820809248555, "acc_norm_stderr": 0.026854257928258875 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2435754189944134, "acc_stderr": 0.014355911964767867, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.014355911964767867 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.545751633986928, "acc_stderr": 0.02850980780262659, "acc_norm": 0.545751633986928, "acc_norm_stderr": 0.02850980780262659 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6109324758842444, "acc_stderr": 0.027690337536485376, "acc_norm": 0.6109324758842444, "acc_norm_stderr": 0.027690337536485376 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.558641975308642, "acc_stderr": 0.027628737155668777, "acc_norm": 0.558641975308642, "acc_norm_stderr": 0.027628737155668777 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3900709219858156, "acc_stderr": 0.02909767559946393, "acc_norm": 0.3900709219858156, "acc_norm_stderr": 0.02909767559946393 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3709256844850065, "acc_stderr": 0.012337391684530312, "acc_norm": 0.3709256844850065, "acc_norm_stderr": 0.012337391684530312 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5110294117647058, "acc_stderr": 0.030365446477275675, "acc_norm": 0.5110294117647058, "acc_norm_stderr": 0.030365446477275675 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5, "acc_stderr": 0.020227834851568375, "acc_norm": 0.5, "acc_norm_stderr": 0.020227834851568375 }, "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": 0.5673469387755102, "acc_stderr": 0.031717528240626645, "acc_norm": 0.5673469387755102, "acc_norm_stderr": 0.031717528240626645 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6417910447761194, "acc_stderr": 0.03390393042268814, "acc_norm": 0.6417910447761194, "acc_norm_stderr": 0.03390393042268814 }, "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": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6900584795321637, "acc_stderr": 0.035469769593931624, "acc_norm": 0.6900584795321637, "acc_norm_stderr": 0.035469769593931624 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.29865361077111385, "mc1_stderr": 0.016021570613768542, "mc2": 0.455854048100616, "mc2_stderr": 0.01483939681310818 }, "harness\|winogrande\|5": { "acc": 0.744277821625888, "acc_stderr": 0.012261253845440473 }, "harness\|gsm8k\|5": { "acc": 0.18726307808946172, "acc_stderr": 0.010745914199510815 } } ``` ## 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 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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]
tfnn/MESHY.AI_363_PLY_Creatures_Labelled	--- license: cc0-1.0 language: - en tags: - glb - 3d - asset - model - mesh - gltf - meshy - meshy.ai - creatures - humans - people - animals - monsters - robots - mythical - dragons pretty_name: MESHY.AI 363 PLY Creatures Labelled size_categories: - n<1K --- - [meshy_creatures.zip](https://huggingface.co/datasets/tfnn/MESHY.AI_363_PLY_Creatures_Labelled/resolve/main/meshy_creatures.zip?download=true) - 363 Samples [PLY](https://paulbourke.net/dataformats/ply/) ([Vertex Colored](https://github.com/VertexColor), Labelled). Refined from [this dataset](https://huggingface.co/datasets/tfnn/MESHY.AI_800_GLB_3D-Assets_Categorised_and_Labelled) but with fixed spelling mistakes and slightly improved labelling this dataset of 363 creatures includes `people`, `animals`, `monsters`, and `robots`. Along with some mythical dragons, mushroom people, frogs, four legged animals with two legs, etc. These models have been scaled to a unit sphere which is a normalised cubic scale multiplied by 0.55 which means they fit perfectly within a unit sphere. This dataset makes for good test cases where you just want to prove a network topology works, hastily, on a reasonably decent and small dataset. All models in this dataset are rougly the same category with the biggest deviation being two legged creatures to four legged creatures or bipedal robots with wheels rather than legs. Each model can range between 16,000 and 45,000 vertices, fairly uniform spacing between each vertex. Models are vertex colored. Special thanks to [MESHY.AI](https://meshy.ai) for generating the models for this dataset using their online service.
mapama247/chess_games_caissabase	--- license: apache-2.0 ---
yzhuang/autotree_pmlb_100000_clean2_sgosdt_l256_dim10_d3_sd0	--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: input_y_clean sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 2364400000 num_examples: 100000 - name: validation num_bytes: 236440000 num_examples: 10000 download_size: 622819971 dataset_size: 2600840000 --- # Dataset Card for "autotree_pmlb_100000_clean2_sgosdt_l256_dim10_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ziyuan111/Urban_Tree_Canopy_in_Durham2	--- license: apache-2.0 language: - en size_categories: - 10K<n<100K --- # Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) ## Project Overview The Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage. ## Background and Rationale Durham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come. ## Data Sources and Methodology ### Data Sources We will leverage the following files from the Durham Trees & Planting Sites Dataset, as found on the Durham Open Data portal: - `GS_TreeInventory.shp` - `Trees_&_Planting_Sites.csv` - `Trees_%26_Planting_Sites.geojson` # Dataset Card for Urban Tree Inventory ## Dataset Description This dataset provides comprehensive information about urban trees within a specified area, including their physical characteristics, environmental benefits, and the economic value they add in terms of ecosystem services. ### Spatial Data (GeoJSON) Format: GeoJSON Content: - Type: `FeatureCollection` - A collection of feature objects. - Features: Each feature object represents a tree and contains: - Type: `Feature` - Geometry: `Point` (includes longitude and latitude of the tree location). - Properties: Detailed information about the tree (some fields may overlap with the CSV structure below). ### Tabular Data (CSV) Format: CSV Columns: - X, Y: Coordinates of the tree location. - OBJECTID: Unique identifier for the tree. - streetaddress: Street address nearest to the tree. - city: City where the tree is located. - zipcode: Zip code for the location of the tree. - facilityid: Identifier for the facility associated with the tree, if any. - present: Indication of whether the tree is currently present. - genus, species, commonname: Botanical and common names of the tree. - plantingdate: Date when the tree was planted. - diameterin: Diameter of the tree trunk in inches. - heightft: Height of the tree in feet. - condition: Health condition of the tree. - contractwork: Indicates if the tree has had any contract work done. - neighborhood: Neighborhood where the tree is located. - program: The program under which the tree was planted. - plantingw: Width of the planting site. - plantingcond: Condition of the planting site. - underpwerlins: Whether the tree is under power lines. - matureheight: The mature height of the tree. - GlobalID: A global unique identifier for the tree. - created_user: The user who created the record. - created_date: The date the record was created. - last_edited_user: The user who last edited the record. - last_edited_date: The date the record was last edited. #### Environmental and Economic Data: - isoprene, monoterpene, vocs: Emissions and absorption data for various compounds. - coremoved_ozperyr, o3removed_ozperyr, etc.: Annual pollutant removal metrics. - o2production_lbperyr: Annual oxygen production. - carbonstorage_lb, carbonstorage_dol: Carbon storage metrics. - grosscarseq_lbperyr, grosscarseq_dolperyr: Gross carbon sequestration. - avoidrunoff_ft2peryr, avoidrunoff_dol2peryr: Metrics related to stormwater runoff avoidance. - totannbenefits_dolperyr: Total annual dollar benefits from the tree. - leafarea_sqft, potevapotran_cuftperyr, etc.: Metrics related to the water cycle. - heating_mbtuperyr, cooling_kwhperyr, etc.: Energy savings related to the tree's impact on building energy use. ### Example Record GeoJSON Feature: ```json { "type": "Feature", "geometry": { "type": "Point", "coordinates": [-78.90863, 36.00441] }, "properties": { "OBJECTID": 2840940, "commonname": "Willow Oak", // Additional properties... } } ``` The `GS_TreeInventory.shp` file encompasses a range of attributes for each record: - OBJECTID: Unique identifier for each record. - streetaddr: Street address where the tree or planting site is located. - city: The city name, which is Durham. - zipcode: Postal code for the location. - facilityid: Identifier possibly linked to a facility or area associated with the tree. - present: Type of feature present, such as a tree or a planting site. - genus: Genus of the tree. - species: Species of the tree. - commonname: Common name of the tree. - plantingda: Date or year range when the tree was planted or the planting site was established. - ... ### Objectives 1. Combine Shapefile and CSV data into a comprehensive geospatial dataset using Python. 2. Apply Python libraries to uncover relationships between tree canopy data and urban development. 3. Provide practical insights and strategies for the expansion of Durham's urban tree canopy. 4. Produce analyses and visualizations with the GeoJSON file. ### Methodology Our analytical process within Google Colab will encompass: - Data Preparation and Integration: Using tools like Geopandas, Pandas, and PyShp to organize and combine spatial and tabular data. - Geospatial Analysis: Applying Shapely and Rtree for spatial analysis, and using SciPy or Statsmodels for statistical correlations. - Visualization and Optimization: Generating maps and graphs with Matplotlib, Seaborn, or Plotly, and utilizing optimization algorithms to suggest optimal planting locations. ## Deliverables 1. A collection of Google Colab Python notebooks that outline our analytical processes. 2. Interactive maps and visualizations that connect tree canopy coverage with urban development metrics. 3. An exhaustive report that contains our findings and recommendations for enhancing the urban canopy. ## Limitations - Computational Resources: The limited computational offerings of Google Colab may pose a challenge to the size of the datasets or the complexity of models we can employ. - Data Quality: The accuracy and currency of the data ultimately affect the precision of our recommendations. - Sociopolitical Considerations: Implementation of our data-driven suggestions must be reviewed within the context of local policy and community input. ## Conclusion DUCAEI aims to create a more verdant and livable urban landscape in Durham through this Python-based analytical project. By laying a strong foundation for data-informed decision-making, we hope to cultivate a thriving, green, and sustainable urban environment.
ainzOulgun/fqavrR	--- license: openrail ---
ctoraman/deprem-tweet-dataset	--- license: cc task_categories: - text-classification - token-classification language: - tr tags: - disaster-relief - disaster - earthquake - tweets - deprem - tweet-classification - ner size_categories: - 1K<n<10K --- Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster The annotated dataset is given at dataset.tsv. We annotate 1,000 tweets in Turkish if tweets call for help (i.e. request rescue, supply, or donation), and their entity tags (person, city, address, status). Column Name Description label Human annotation if tweet calls for help (binary classification) entities Human annotation of entity tags (i.e. person, city, address, and status). The format is [START_INDEX]:[END_INDEX]%[TAG_TYPE]. tweet_id Tweet ID from Twitter API. Other details can be found at https://github.com/avaapm/deprem Citation If you make use of this dataset, please cite following paper. @misc{toraman2023earthquake, doi = {10.48550/ARXIV.2302.13403}, url = {https://arxiv.org/abs/2302.13403}, author = {Toraman, Cagri and Kucukkaya, Izzet Emre and Ozcelik, Oguzhan and Sahin, Umitcan}, keywords = {Social and Information Networks (cs.SI), Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster}, publisher = {arXiv}, year = {2023}, copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} }
mpasila/DarkViperAU-Essays	--- language: - en --- This is just the raw text extract of his essays. This will be cleaned up later.
allenai/multixscience_sparse_max	--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization paperswithcode_id: multi-xscience pretty_name: Multi-XScience --- This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used: - __query__: The `related_work` field of each example - __corpus__: The union of all documents in the `train`, `validation` and `test` splits - __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings - __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==20` Retrieval results on the `train` set: \| Recall@100 \| Rprec \| Precision@k \| Recall@k \| \| ----------- \| ----------- \| ----------- \| ----------- \| \| 0.5482 \| 0.2243 \| 0.0547 \| 0.4063 \| Retrieval results on the `validation` set: \| Recall@100 \| Rprec \| Precision@k \| Recall@k \| \| ----------- \| ----------- \| ----------- \| ----------- \| \| 0.5476 \| 0.2209 \| 0.0553 \| 0.4026 \| Retrieval results on the `test` set: \| Recall@100 \| Rprec \| Precision@k \| Recall@k \| \| ----------- \| ----------- \| ----------- \| ----------- \| \| 0.5480 \| 0.2272 \| 0.055 \| 0.4039 \|
liuyanchen1015/MULTI_VALUE_rte_plural_postposed	--- 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: 890889 num_examples: 2389 - name: train num_bytes: 776257 num_examples: 1989 download_size: 1045708 dataset_size: 1667146 --- # Dataset Card for "MULTI_VALUE_rte_plural_postposed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/charlotte_fireemblem	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of charlotte (Fire Emblem) This is the dataset of charlotte (Fire Emblem), containing 285 images and their tags. The core tags of this character are `blonde_hair, long_hair, breasts, bow, blue_eyes, hair_bow, large_breasts, bangs, white_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 \| 285 \| 304.23 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 285 \| 183.26 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 665 \| 377.56 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 285 \| 275.84 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 665 \| 516.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/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/charlotte_fireemblem', 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 \| 26 \| ![](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, hetero, solo_focus, 1boy, penis, pussy, nipples, blush, uncensored, vaginal, nude, open_mouth, navel, sex_from_behind, spread_legs, testicles \| \| 1 \| 18 \| ![](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, 1girl, hetero, solo_focus, penis, nipples, paizuri, blush, nude, cum_on_breasts, smile, facial, uncensored \| \| 2 \| 20 \| ![](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, cleavage, solo, smile, navel, midriff, looking_at_viewer, shoulder_armor, bikini_armor, white_background, simple_background, spikes \| \| 3 \| 11 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, solo, huge_breasts, looking_at_viewer, smile, thick_thighs, blush, blunt_bangs, short_shorts, ass, cleavage, simple_background, thighhighs, blue_background, denim_shorts, from_behind, looking_back \| \| 4 \| 10 \| ![](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, huge_penis, solo, uncensored, blush, erection, nipples, nude, open_mouth, testicles, navel, heart, animal_penis, blunt_bangs, ejaculation, futanari_masturbation, rolling_eyes, spread_legs, thighhighs, tongue, veiny_penis \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| hetero \| solo_focus \| 1boy \| penis \| pussy \| nipples \| blush \| uncensored \| vaginal \| nude \| open_mouth \| navel \| sex_from_behind \| spread_legs \| testicles \| paizuri \| cum_on_breasts \| smile \| facial \| cleavage \| solo \| midriff \| looking_at_viewer \| shoulder_armor \| bikini_armor \| white_background \| simple_background \| spikes \| huge_breasts \| thick_thighs \| blunt_bangs \| short_shorts \| ass \| thighhighs \| blue_background \| denim_shorts \| from_behind \| looking_back \| huge_penis \| erection \| heart \| animal_penis \| ejaculation \| futanari_masturbation \| rolling_eyes \| tongue \| veiny_penis \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------\|:-------------\|:-------\|:--------\|:--------\|:----------\|:--------\|:-------------\|:----------\|:-------\|:-------------\|:--------\|:------------------\|:--------------\|:------------\|:----------\|:-----------------\|:--------\|:---------\|:-----------\|:-------\|:----------\|:--------------------\|:-----------------\|:---------------\|:-------------------\|:--------------------\|:---------\|:---------------\|:---------------\|:--------------\|:---------------\|:------\|:-------------\|:------------------\|:---------------\|:--------------\|:---------------\|:-------------\|:-----------\|:--------\|:---------------\|:--------------\|:------------------------\|:---------------\|:---------\|:--------------\| \| 0 \| 26 \| ![](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 \| 18 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 20 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 11 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| \| X \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 4 \| 10 \| ![](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 \|
CyberHarem/silk_fireemblem	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of silk (Fire Emblem) This is the dataset of silk (Fire Emblem), containing 107 images and their tags. The core tags of this character are `blue_hair, short_hair, breasts, blue_eyes, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 107 \| 92.85 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 107 \| 62.85 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 236 \| 118.92 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 107 \| 85.12 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 236 \| 149.20 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/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/silk_fireemblem', 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 \| 64 \| ![](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, dress, simple_background, smile, looking_at_viewer, veil, long_sleeves, white_background \| \| 1 \| 11 \| ![](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) \| hetero, solo_focus, 1girl, blush, vaginal, large_breasts, nipples, open_mouth, mosaic_censoring, multiple_boys, completely_nude, cum_in_pussy, multiple_penises, spread_legs, 1boy, sweat, double_handjob, gangbang, green_eyes, pregnant, tongue \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| dress \| simple_background \| smile \| looking_at_viewer \| veil \| long_sleeves \| white_background \| hetero \| solo_focus \| blush \| vaginal \| large_breasts \| nipples \| open_mouth \| mosaic_censoring \| multiple_boys \| completely_nude \| cum_in_pussy \| multiple_penises \| spread_legs \| 1boy \| sweat \| double_handjob \| gangbang \| green_eyes \| pregnant \| tongue \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------\|:--------------------\|:--------\|:--------------------\|:-------\|:---------------\|:-------------------\|:---------\|:-------------\|:--------\|:----------\|:----------------\|:----------\|:-------------\|:-------------------\|:----------------\|:------------------\|:---------------\|:-------------------\|:--------------\|:-------\|:--------\|:-----------------\|:-----------\|:-------------\|:-----------\|:---------\| \| 0 \| 64 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 11 \| ![](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 \|
FelixdoingAI/IP2P-adwm-128-5000	--- dataset_info: features: - name: original_prompt dtype: string - name: original_image dtype: image - name: edit_prompt dtype: string - name: edited_prompt dtype: string - name: edited_image dtype: image - name: adversarial_image dtype: image splits: - name: train num_bytes: 574509255.0 num_examples: 5000 download_size: 574446939 dataset_size: 574509255.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/namba_eri_idolmastercinderellagirls	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of namba_eri (THE iDOLM@STER: Cinderella Girls) This is the dataset of namba_eri (THE iDOLM@STER: Cinderella Girls), containing 32 images and their tags. The core tags of this character are `brown_hair, short_hair, hair_ornament, hairclip, green_eyes, wavy_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 \| 32 \| 20.39 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 32 \| 18.38 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 55 \| 30.54 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 32 \| 20.25 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 55 \| 32.91 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/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/namba_eri_idolmastercinderellagirls', 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 \| 13 \| ![](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, card_(medium), character_name, sun_symbol, skirt, open_mouth, orange_background, :d, looking_at_viewer, bow, breasts, grin \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| card_(medium) \| character_name \| sun_symbol \| skirt \| open_mouth \| orange_background \| :d \| looking_at_viewer \| bow \| breasts \| grin \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:----------------\|:-----------------\|:-------------\|:--------\|:-------------\|:--------------------\|:-----\|:--------------------\|:------\|:----------\|:-------\| \| 0 \| 13 \| ![](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 \|
autoevaluate/autoeval-eval-xsum-default-8e4fa8-60494145409	--- type: predictions tags: - autotrain - evaluation datasets: - xsum eval_info: task: summarization model: morenolq/bart-base-xsum metrics: [] dataset_name: xsum dataset_config: default dataset_split: test col_mapping: text: document target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: morenolq/bart-base-xsum * Dataset: xsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@zuzannad1](https://huggingface.co/zuzannad1) for evaluating this model.
yzhuang/autotree_automl_heloc_gosdt_l512_d3_sd3	--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: int64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: int64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 11682400000 num_examples: 100000 - name: validation num_bytes: 1168240000 num_examples: 10000 download_size: 1508311822 dataset_size: 12850640000 --- # Dataset Card for "autotree_automl_heloc_gosdt_l512_d3_sd3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davidreyblanco/distilabel-math-instructions	--- dataset_info: features: - name: instructions dtype: string splits: - name: train num_bytes: 169849 num_examples: 1840 download_size: 56464 dataset_size: 169849 configs: - config_name: default data_files: - split: train path: data/train-* ---
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-110000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 969978 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
mHossain/final_train_v2_90000	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: input_text dtype: string - name: target_text dtype: string - name: prefix dtype: string splits: - name: train num_bytes: 9136964.7 num_examples: 27000 - name: test num_bytes: 1015218.3 num_examples: 3000 download_size: 4451000 dataset_size: 10152183.0 --- # Dataset Card for "final_train_v2_90000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
naorm/desktop-blip-large	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 12745883.0 num_examples: 51 download_size: 12428402 dataset_size: 12745883.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
yiyic/eval_clir_mtg	--- dataset_info: features: - name: text dtype: string splits: - name: mtg_en num_bytes: 48454 num_examples: 500 - name: mtg_de num_bytes: 57186 num_examples: 500 - name: mtg_es num_bytes: 51263 num_examples: 500 - name: mtg_fr num_bytes: 59140 num_examples: 500 - name: nq_en num_bytes: 306325 num_examples: 500 - name: en_rt num_bytes: 49483 num_examples: 500 - name: de_en_multi8_test1 num_bytes: 1308045 num_examples: 1000 - name: de_fr_multi8_test1 num_bytes: 1310347 num_examples: 1000 - name: de_es_multi8_test1 num_bytes: 1310100 num_examples: 1000 - name: en_de_multi8_test1 num_bytes: 1120561 num_examples: 1000 - name: en_fr_multi8_test1 num_bytes: 1122148 num_examples: 1000 - name: en_es_multi8_test1 num_bytes: 1121901 num_examples: 1000 - name: es_en_multi8_test1 num_bytes: 1093157 num_examples: 1000 - name: es_fr_multi8_test1 num_bytes: 1095459 num_examples: 1000 - name: es_de_multi8_test1 num_bytes: 1093872 num_examples: 1000 - name: fr_en_multi8_test1 num_bytes: 1134440 num_examples: 1000 - name: fr_de_multi8_test1 num_bytes: 1135155 num_examples: 1000 - name: fr_es_multi8_test1 num_bytes: 1136495 num_examples: 1000 download_size: 9037857 dataset_size: 14553531 configs: - config_name: default data_files: - split: mtg_en path: data/mtg_en-* - split: mtg_de path: data/mtg_de-* - split: mtg_es path: data/mtg_es-* - split: mtg_fr path: data/mtg_fr-* - split: nq_en path: data/nq_en-* - split: en_rt path: data/en_rt-* - 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-* ---
YashaP/tinyscitldr	--- dataset_info: features: - name: instruction dtype: string - name: output sequence: string splits: - name: train num_bytes: 1368087 num_examples: 1000 download_size: 737186 dataset_size: 1368087 configs: - config_name: default data_files: - split: train path: data/train-* ---
foilfoilfoil/LaminiChatML	--- license: other ---
Rasu23/iapp_chosen_reject_orpov1	--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: question dtype: string - name: context dtype: string - name: index_column dtype: int64 - name: text_prompt dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 33360071 num_examples: 5610 download_size: 5948625 dataset_size: 33360071 configs: - config_name: default data_files: - split: train path: data/train-* ---
SEACrowd/posp	--- tags: - pos-tagging language: - ind --- # posp POSP is a POS Tagging dataset containing 8400 sentences, collected from Indonesian news website with 26 POS tag classes. The POS tag labels follow the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention. POSP dataset is splitted into 3 sets with 6720 train, 840 validation, and 840 test data. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @inproceedings{hoesen2018investigating, title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger}, author={Devin Hoesen and Ayu Purwarianti}, booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)}, pages={35--38}, year={2018}, organization={IEEE} } @inproceedings{wilie2020indonlu, title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding}, author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti}, booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing}, year={2020} } ``` ## License Creative Common Attribution Share-Alike 4.0 International ## Homepage [https://github.com/IndoNLP/indonlu](https://github.com/IndoNLP/indonlu) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3	--- dataset_info: features: - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train_qa num_bytes: 697367 num_examples: 6000 - name: train_ic_qa num_bytes: 4540536 num_examples: 6000 - name: train_recite_qa num_bytes: 4546536 num_examples: 6000 - name: eval_qa num_bytes: 752802 num_examples: 6489 - name: eval_ic_qa num_bytes: 4906186 num_examples: 6489 - name: eval_recite_qa num_bytes: 4912675 num_examples: 6489 - name: all_docs num_bytes: 7126313 num_examples: 10925 - name: all_docs_eval num_bytes: 7125701 num_examples: 10925 - name: train num_bytes: 3818906 num_examples: 6000 - name: validation num_bytes: 4103798 num_examples: 6489 download_size: 26446330 dataset_size: 42530820 configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_ic_qa path: data/train_ic_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_ic_qa path: data/eval_ic_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: all_docs path: data/all_docs-* - split: all_docs_eval path: data/all_docs_eval-* - split: train path: data/train-* - split: validation path: data/validation-* ---
CyberHarem/rosmontis_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of rosmontis/ロスモンティス/迷迭香 (Arknights) This is the dataset of rosmontis/ロスモンティス/迷迭香 (Arknights), containing 500 images and their tags. The core tags of this character are `animal_ears, cat_ears, long_hair, cat_girl, animal_ear_fluff, green_eyes, grey_hair, white_hair, tail, cat_tail`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:---------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 872.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 425.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_arknights/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1280 \| 955.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_arknights/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 732.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1280 \| 1.44 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_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/rosmontis_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 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, black_gloves, fingerless_gloves, hood_down, jacket, solo, white_dress, holding_book, infection_monitor_(arknights), looking_at_viewer, closed_mouth, earpiece, hooded_cloak \| \| 1 \| 6 \| ![](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_footwear, black_gloves, closed_mouth, fingerless_gloves, full_body, solo, white_dress, boots, looking_at_viewer, earpiece, hood_down, hooded_cloak, infection_monitor_(arknights), thigh_strap \| \| 2 \| 10 \| ![](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, hood_down, looking_at_viewer, solo, upper_body, jacket, blush, simple_background, white_background, closed_mouth \| \| 3 \| 13 \| ![](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, collar, looking_at_viewer, sleeveless_dress, solo, white_dress, bare_shoulders, official_alternate_costume, barefoot, twintails, closed_mouth, full_body, very_long_hair, bare_legs, feet, mechanical_wings, toes, white_background, colored_eyelashes, simple_background, sitting \| \| 4 \| 10 \| ![](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) \| 1boy, 1girl, blush, hetero, sex, nipples, sweat, vaginal, penis, solo_focus, open_mouth, small_breasts, black_gloves, fingerless_gloves, spread_legs, thigh_strap, dress, mosaic_censoring, navel, straddling, closed_eyes, coat, cum_in_pussy, girl_on_top, looking_at_viewer, missionary, on_back, pov \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_gloves \| fingerless_gloves \| hood_down \| jacket \| solo \| white_dress \| holding_book \| infection_monitor_(arknights) \| looking_at_viewer \| closed_mouth \| earpiece \| hooded_cloak \| black_footwear \| full_body \| boots \| thigh_strap \| upper_body \| blush \| simple_background \| white_background \| collar \| sleeveless_dress \| bare_shoulders \| official_alternate_costume \| barefoot \| twintails \| very_long_hair \| bare_legs \| feet \| mechanical_wings \| toes \| colored_eyelashes \| sitting \| 1boy \| hetero \| sex \| nipples \| sweat \| vaginal \| penis \| solo_focus \| open_mouth \| small_breasts \| spread_legs \| dress \| mosaic_censoring \| navel \| straddling \| closed_eyes \| coat \| cum_in_pussy \| girl_on_top \| missionary \| on_back \| pov \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:--------------------\|:------------\|:---------\|:-------\|:--------------\|:---------------\|:--------------------------------\|:--------------------\|:---------------\|:-----------\|:---------------\|:-----------------\|:------------\|:--------\|:--------------\|:-------------\|:--------\|:--------------------\|:-------------------\|:---------\|:-------------------\|:-----------------\|:-----------------------------\|:-----------\|:------------\|:-----------------\|:------------\|:-------\|:-------------------\|:-------\|:--------------------\|:----------\|:-------\|:---------\|:------\|:----------\|:--------\|:----------\|:--------\|:-------------\|:-------------\|:----------------\|:--------------\|:--------\|:-------------------\|:--------\|:-------------\|:--------------\|:-------\|:---------------\|:--------------\|:-------------\|:----------\|:------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 13 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 10 \| ![](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 \| X \|
satyambarnwal/cartoonizer-dataset	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 31656230.0 num_examples: 50 download_size: 31658428 dataset_size: 31656230.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
asun17904/wiki2017_bank_examples	--- dataset_info: features: - name: section_texts sequence: string - name: section_titles sequence: string - name: title dtype: string - name: soi sequence: string - name: other sequence: string splits: - name: train num_bytes: 3467283652 num_examples: 163992 download_size: 0 dataset_size: 3467283652 --- # Dataset Card for "wiki2017_bank_examples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
atmallen/companies_azaria_mitchell	--- dataset_info: features: - name: statement dtype: string - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 80207.2 num_examples: 960 - name: test num_bytes: 20051.8 num_examples: 240 download_size: 34846 dataset_size: 100259.0 --- # Dataset Card for "companies_azaria_mitchell" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Multimodal-Fatima/VQAv2_test_split_4

--- 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: id dtype: int64 - 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: 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: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - 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 splits: - name: test num_bytes: 9135639447.0 num_examples: 44779 download_size: 1867482751 dataset_size: 9135639447.0 --- # Dataset Card for "VQAv2_test_split_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

EleutherAI/hendrycks_math

--- license: mit ---

aditnnda/gacoanGenteng

--- license: apache-2.0 ---

ShrinivasSK/en_hi_1

--- dataset_info: features: - name: idx dtype: int64 - name: src dtype: string - name: tgt dtype: string splits: - name: train num_bytes: 6435127.8 num_examples: 18000 - name: test num_bytes: 715014.2 num_examples: 2000 download_size: 3824291 dataset_size: 7150142.0 --- # Dataset Card for "data_hi_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Tippawan/test2-data-semi-trainulb-r3-WLV

--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: int64 - name: prob sequence: float64 - name: ifpass sequence: int64 - name: pred dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 71056510 num_examples: 37243 download_size: 12950153 dataset_size: 71056510 configs: - config_name: default data_files: - split: train path: data/train-* ---

andersonbcdefg/filtered_triples_with_margins

--- dataset_info: features: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string - name: source dtype: string - name: qp_sim dtype: float32 - name: qn_sim dtype: float32 - name: pn_sim dtype: float32 - name: margin dtype: float64 splits: - name: train num_bytes: 1134373304 num_examples: 1246705 download_size: 692171169 dataset_size: 1134373304 configs: - config_name: default data_files: - split: train path: data/train-* ---

Tap-M/Llama2-Uncensored-Orca-Lima-Style

--- license: agpl-3.0 ---

hatakeyama-llm-team/WikiBookJa

--- license: cc-by-3.0 ---

Sharathhebbar24/BeaverTails_filtered

--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - text-generation dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 78049982 num_examples: 161784 download_size: 40376094 dataset_size: 78049982 configs: - config_name: default data_files: - split: train path: data/train-* --- # Beaver Tails This is a cleansed version of [PKU-Alignment/BeaverTails](https://huggingface.co/datasets/PKU-Alignment/BeaverTails) It has two version based on the sensitivity ## Filtered ### Usage ```python from datasets import load_dataset dataset = load_dataset("Sharathhebbar24/BeaverTails_filtered", split="train") ``` ## Unfiltered ### Usage ```python from datasets import load_dataset dataset = load_dataset("Sharathhebbar24/BeaverTails_unfiltered", split="train") ```

mrbrain404/my_datasets

--- license: other ---

elvis-d/train

--- license: apache-2.0 ---

mask-distilled-one-sec-cv12/chunk_165

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1171949260 num_examples: 230155 download_size: 1197293781 dataset_size: 1171949260 --- # Dataset Card for "chunk_165" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BelalElhossany/mgb2_audios_transcriptions

--- dataset_info: features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 1181823173.4 num_examples: 5842 download_size: 1141789958 dataset_size: 1181823173.4 --- # Dataset Card for "mgb2_audios_transcriptions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceM4/VQAv2_modif-Dummy

Invalid username or password.

Duskfallcrew/Alex_Brightman

--- license: creativeml-openrail-m task_categories: - text-to-image language: - en tags: - lora - lycoris pretty_name: Alex Brightman size_categories: - 1K<n<10K --- Alex Brightman Lycoris

mnoukhov/openai_summarize_tldr_rbaseline

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: prompt dtype: string - name: label dtype: string - name: reward_baseline dtype: float32 splits: - name: train num_bytes: 181611007 num_examples: 116722 - name: valid num_bytes: 3120685 num_examples: 2000 download_size: 113145941 dataset_size: 184731692 --- # Dataset Card for "openai_summarize_tldr_rbaseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

deepapaikar/Sentence_pairs

--- license: apache-2.0 dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 1901011 num_examples: 12560 download_size: 991351 dataset_size: 1901011 configs: - config_name: default data_files: - split: train path: data/train-* ---

Ediudo/sal

--- license: openrail ---

DBQ/Fendi.Standard.Categories.Italy

--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Italy - Fendi - Fashion Standard Categories tags: - webscraping - ecommerce - Fendi - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: dbq_prd_type dtype: string - name: competence_date dtype: string - name: website_name dtype: string - name: product_code dtype: string - name: tag_field dtype: string splits: - name: train num_bytes: 271821 num_examples: 3055 download_size: 56203 dataset_size: 271821 --- # Fendi web scraped data ## About the website In the **EMEA** region, particularly in **Italy**, the **luxury fashion industry** has an immense influence and it significantly contributes to Italys economy. Brands such as **Fendi** are prominent players in this sector. Emphasizing on haute couture, ready-to-wear clothing, leather goods, shoes, fragrances, eyewear, timepieces and accessories, the industry has seen significant growth with the adoption of **Ecommerce**. Our dataset provides an insightful look at Ecommerce product tags data on Fendi products in Italy. The research data aids in understanding the online buying behavior of luxury fashion consumers in Italy, providing great potential for market analysis and strategy development. ## Link to **dataset** [Italy - Fendi - Fashion Standard Categories dataset](https://www.databoutique.com/buy-data-page/Fendi%20Standard%20Categories%20Italy/r/recrd9vOREnRQ68V1)

rgricardo/Takubgroup

--- license: openrail ---

Susmita1302/images

--- license: mit ---

vadimpetrov/common_voice_13_0_ru_pseudo_labelled

--- dataset_info: config_name: ru features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 991934032.405 num_examples: 26269 - name: validation num_bytes: 401622741.215 num_examples: 10185 - name: test num_bytes: 391004723.912 num_examples: 10186 download_size: 1724461730 dataset_size: 1784561497.5319998 configs: - config_name: ru data_files: - split: train path: ru/train-* - split: validation path: ru/validation-* - split: test path: ru/test-* ---

cvzion/dataset-dqg1

--- dataset_info: features: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 57020 num_examples: 238 download_size: 16985 dataset_size: 57020 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/idw_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of idw/IDW/IDW (Girls' Frontline) This is the dataset of idw/IDW/IDW (Girls' Frontline), containing 125 images and their tags. The core tags of this character are `animal_ears, blonde_hair, cat_ears, blue_eyes, long_hair, twintails, hair_ornament, bangs, hair_between_eyes, tail, fang, hairclip, cat_tail, breasts, animal_ear_fluff`, 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 | 125 | 142.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 125 | 82.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 295 | 177.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 125 | 126.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 295 | 249.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/idw_girlsfrontline/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/idw_girlsfrontline', 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 | 8 | ![](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, fingerless_gloves, shorts, boots, headset, looking_at_viewer, necktie, open_mouth, suspenders, holding_gun, knee_pads, smile, full_body, panties, submachine_gun, :3, sleeves_rolled_up, white_shirt | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, fingerless_gloves, headset, open_mouth, suspender_shorts, collared_shirt, white_shirt, black_shorts, holding_gun, sleeves_rolled_up, :d, blue_panties, character_name, cowboy_shot, short_shorts, submachine_gun, 2girls, looking_at_viewer, solo_focus, striped_necktie, v-shaped_eyebrows | | 2 | 11 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, elbow_gloves, solo, black_dress, black_gloves, official_alternate_costume, open_mouth, smile, bare_shoulders, choker, small_breasts, collarbone, looking_at_viewer, strapless_dress, sunglasses, simple_background, thigh_strap, :3, gun, tail_ribbon, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | fingerless_gloves | shorts | boots | headset | looking_at_viewer | necktie | open_mouth | suspenders | holding_gun | knee_pads | smile | full_body | panties | submachine_gun | :3 | sleeves_rolled_up | white_shirt | suspender_shorts | collared_shirt | black_shorts | :d | blue_panties | character_name | cowboy_shot | short_shorts | 2girls | solo_focus | striped_necktie | v-shaped_eyebrows | elbow_gloves | black_dress | black_gloves | official_alternate_costume | bare_shoulders | choker | small_breasts | collarbone | strapless_dress | sunglasses | simple_background | thigh_strap | gun | tail_ribbon | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:---------|:--------|:----------|:--------------------|:----------|:-------------|:-------------|:--------------|:------------|:--------|:------------|:----------|:-----------------|:-----|:--------------------|:--------------|:-------------------|:-----------------|:---------------|:-----|:---------------|:-----------------|:--------------|:---------------|:---------|:-------------|:------------------|:--------------------|:---------------|:--------------|:---------------|:-----------------------------|:-----------------|:---------|:----------------|:-------------|:------------------|:-------------|:--------------------|:--------------|:------|:--------------|:-------------------| | 0 | 8 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | | | X | X | | X | | X | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 2 | 11 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | | | | X | | X | | | | X | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

timm/imagenet-12k-wds

--- license: other license_name: imagenet license_link: https://www.image-net.org/download.php task_categories: - image-classification pretty_name: ImageNet-12k size_categories: - 10M<n<100M extra_gated_prompt: >- By clicking on “Access repository” below, you also agree to ImageNet Terms of Access: [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement. tags: - webdataset --- ## Dataset Description - **Homepage:** https://image-net.org/index.php - **Repository:** https://github.com/rwightman/imagenet-12k - **Paper:** https://arxiv.org/abs/1409.0575 ### Dataset Summary This is a filtered copy of the full [ImageNet](https://www.image-net.org/) dataset consisting of the top 11821 (of 21841) classes by number of samples. It has been used to pretrain a number of `in12k` models in `timm`. The code and metadata for building this dataset from the original full ImageNet can be found at https://github.com/rwightman/imagenet-12k NOTE: This subset was filtered from the original `fall11` ImageNet release which has been replaced by the `winter21` release which removes close to 3000 synsets containing people, a number of these are of an offensive or sensitive nature. There is work in progress to filter a similar dataset from `winter21`, and there is already [ImageNet-21k-P](https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_instructions.md) but with different thresholds & preprocessing steps. ### Data Splits Unlike ImageNet-1k (ILSVRC 2012), the full ImageNet dataset has no defined splits. This subset includes a validation split consiting of 40 samples per 11821 classes. #### Train * `imagenet12k-train-{0000..1023}.tar` * 12129687 samples over 1024 shards #### Validation * `imagenet12k-validation-{0000..0511}.tar` * 472840 samples over 512 shards ### Processing I performed some processing while sharding this dataset: * All exif tags not related to color space were removed * All images with width or height < 48 were removed. * All images with the smallest edge > 600 were resized, maintaining aspect so that they were = 600. Improving size & decoding time uniformity for typical pretrain use cases. * Images were pre-shuffled across the shards ## Additional Information ### Dataset Curators Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848): - Olga Russakovsky - Jia Deng - Hao Su - Jonathan Krause - Sanjeev Satheesh - Wei Dong - Richard Socher - Li-Jia Li - Kai Li - Sean Ma - Zhiheng Huang - Andrej Karpathy - Aditya Khosla - Michael Bernstein - Alexander C Berg - Li Fei-Fei ### Licensing Information In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 1. The law of the State of New Jersey shall apply to all disputes under this agreement. ### Citation Information ```bibtex @article{imagenet15russakovsky, Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, Title = { {ImageNet Large Scale Visual Recognition Challenge} }, Year = {2015}, journal = {International Journal of Computer Vision (IJCV)}, doi = {10.1007/s11263-015-0816-y}, volume={115}, number={3}, pages={211-252} } ```

RKnijn/ds-llama2

--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: source dtype: string splits: - name: train num_bytes: 51117417 num_examples: 126287 download_size: 26326368 dataset_size: 51117417 configs: - config_name: default data_files: - split: train path: data/train-* ---

daze-unlv/medmcqa-mistral-FT-1000

--- license: apache-2.0 ---

Mariofm02/cards_images

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ace of clubs '1': ace of diamonds '2': ace of hearts '3': ace of spades '4': eight of clubs '5': eight of diamonds '6': eight of hearts '7': eight of spades '8': five of clubs '9': five of diamonds '10': five of hearts '11': five of spades '12': four of clubs '13': four of diamonds '14': four of hearts '15': four of spades '16': jack of clubs '17': jack of diamonds '18': jack of hearts '19': jack of spades '20': king of clubs '21': king of diamonds '22': king of hearts '23': king of spades '24': nine of clubs '25': nine of diamonds '26': nine of hearts '27': nine of spades '28': queen of clubs '29': queen of diamonds '30': queen of hearts '31': queen of spades '32': seven of clubs '33': seven of diamonds '34': seven of hearts '35': seven of spades '36': six of clubs '37': six of diamonds '38': six of hearts '39': six of spades '40': ten of clubs '41': ten of diamonds '42': ten of hearts '43': ten of spades '44': three of clubs '45': three of diamonds '46': three of hearts '47': three of spades '48': two of clubs '49': two of diamonds '50': two of hearts '51': two of spades splits: - name: train num_bytes: 130695849.8739789 num_examples: 6603 - name: test num_bytes: 23038296.14202111 num_examples: 1166 download_size: 150881036 dataset_size: 153734146.016 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

rlinares2/chatbot_arena_embeddings_adav3

--- dataset_info: features: - name: question_embedding sequence: float64 - name: answer_embeddings_a sequence: float64 - name: answer_embeddings_b sequence: float64 splits: - name: train num_bytes: 885024000 num_examples: 24000 download_size: 647518122 dataset_size: 885024000 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_steve-cse__MelloGPT

--- pretty_name: Evaluation run of steve-cse/MelloGPT dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [steve-cse/MelloGPT](https://huggingface.co/steve-cse/MelloGPT) 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_steve-cse__MelloGPT\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T18:07:59.704375](https://huggingface.co/datasets/open-llm-leaderboard/details_steve-cse__MelloGPT/blob/main/results_2023-12-16T18-07-59.704375.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\": 0.5580941639624716,\n\ \ \"acc_stderr\": 0.03400113351875304,\n \"acc_norm\": 0.5629530798562845,\n\ \ \"acc_norm_stderr\": 0.03471812426856797,\n \"mc1\": 0.38555691554467564,\n\ \ \"mc1_stderr\": 0.017038839010591673,\n \"mc2\": 0.5560955080575972,\n\ \ \"mc2_stderr\": 0.014788612119825833\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5247440273037542,\n \"acc_stderr\": 0.014593487694937738,\n\ \ \"acc_norm\": 0.53839590443686,\n \"acc_norm_stderr\": 0.01456824555029636\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5646285600477993,\n\ \ \"acc_stderr\": 0.004947922692688834,\n \"acc_norm\": 0.761202947619996,\n\ \ \"acc_norm_stderr\": 0.004254771367531346\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4074074074074074,\n\ \ \"acc_stderr\": 0.04244633238353229,\n \"acc_norm\": 0.4074074074074074,\n\ \ \"acc_norm_stderr\": 0.04244633238353229\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5460526315789473,\n \"acc_stderr\": 0.04051646342874142,\n\ \ \"acc_norm\": 0.5460526315789473,\n \"acc_norm_stderr\": 0.04051646342874142\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.49,\n\ \ \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \ \ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.030000485448675986,\n\ \ \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.030000485448675986\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\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5549132947976878,\n\ \ \"acc_stderr\": 0.03789401760283648,\n \"acc_norm\": 0.5549132947976878,\n\ \ \"acc_norm_stderr\": 0.03789401760283648\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3508771929824561,\n\ \ \"acc_stderr\": 0.04489539350270699,\n \"acc_norm\": 0.3508771929824561,\n\ \ \"acc_norm_stderr\": 0.04489539350270699\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155254,\n \"\ acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155254\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n\ \ \"acc_stderr\": 0.043062412591271526,\n \"acc_norm\": 0.36507936507936506,\n\ \ \"acc_norm_stderr\": 0.043062412591271526\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\"\ : 0.6548387096774193,\n \"acc_stderr\": 0.02704574657353433,\n \"\ acc_norm\": 0.6548387096774193,\n \"acc_norm_stderr\": 0.02704574657353433\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.43349753694581283,\n \"acc_stderr\": 0.03486731727419872,\n \"\ acc_norm\": 0.43349753694581283,\n \"acc_norm_stderr\": 0.03486731727419872\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.03663974994391244,\n\ \ \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.03663974994391244\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"\ acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7409326424870466,\n \"acc_stderr\": 0.031618779179354115,\n\ \ \"acc_norm\": 0.7409326424870466,\n \"acc_norm_stderr\": 0.031618779179354115\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5307692307692308,\n \"acc_stderr\": 0.025302958890850154,\n\ \ \"acc_norm\": 0.5307692307692308,\n \"acc_norm_stderr\": 0.025302958890850154\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815635,\n \ \ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815635\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5630252100840336,\n \"acc_stderr\": 0.03221943636566196,\n \ \ \"acc_norm\": 0.5630252100840336,\n \"acc_norm_stderr\": 0.03221943636566196\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\"\ : 0.7174311926605504,\n \"acc_stderr\": 0.019304243497707152,\n \"\ acc_norm\": 0.7174311926605504,\n \"acc_norm_stderr\": 0.019304243497707152\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.48148148148148145,\n \"acc_stderr\": 0.03407632093854052,\n \"\ acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.03407632093854052\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6764705882352942,\n \"acc_stderr\": 0.032834720561085606,\n \"\ acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.032834720561085606\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.679324894514768,\n \"acc_stderr\": 0.0303819319499904,\n \ \ \"acc_norm\": 0.679324894514768,\n \"acc_norm_stderr\": 0.0303819319499904\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6367713004484304,\n\ \ \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n\ \ \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847834,\n\ \ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847834\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6363636363636364,\n \"acc_stderr\": 0.043913262867240704,\n \"\ acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.043913262867240704\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6851851851851852,\n\ \ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\ \ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n\ \ \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489122,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489122\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.04656147110012351,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.04656147110012351\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077788,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077788\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526094\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7471264367816092,\n\ \ \"acc_stderr\": 0.015543377313719681,\n \"acc_norm\": 0.7471264367816092,\n\ \ \"acc_norm_stderr\": 0.015543377313719681\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5982658959537572,\n \"acc_stderr\": 0.026394104177643637,\n\ \ \"acc_norm\": 0.5982658959537572,\n \"acc_norm_stderr\": 0.026394104177643637\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25251396648044694,\n\ \ \"acc_stderr\": 0.014530330201468647,\n \"acc_norm\": 0.25251396648044694,\n\ \ \"acc_norm_stderr\": 0.014530330201468647\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6405228758169934,\n \"acc_stderr\": 0.027475969910660952,\n\ \ \"acc_norm\": 0.6405228758169934,\n \"acc_norm_stderr\": 0.027475969910660952\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6141479099678456,\n\ \ \"acc_stderr\": 0.027648149599751464,\n \"acc_norm\": 0.6141479099678456,\n\ \ \"acc_norm_stderr\": 0.027648149599751464\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.027339546640662734,\n\ \ \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.027339546640662734\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.375886524822695,\n \"acc_stderr\": 0.028893955412115882,\n \ \ \"acc_norm\": 0.375886524822695,\n \"acc_norm_stderr\": 0.028893955412115882\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.39765319426336376,\n\ \ \"acc_stderr\": 0.012499840347460642,\n \"acc_norm\": 0.39765319426336376,\n\ \ \"acc_norm_stderr\": 0.012499840347460642\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.030161911930767102,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.030161911930767102\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5277777777777778,\n \"acc_stderr\": 0.020196594933541194,\n \ \ \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.020196594933541194\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6530612244897959,\n \"acc_stderr\": 0.030472526026726492,\n\ \ \"acc_norm\": 0.6530612244897959,\n \"acc_norm_stderr\": 0.030472526026726492\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7611940298507462,\n\ \ \"acc_stderr\": 0.03014777593540922,\n \"acc_norm\": 0.7611940298507462,\n\ \ \"acc_norm_stderr\": 0.03014777593540922\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5,\n \ \ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.03892494720807614\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7309941520467836,\n \"acc_stderr\": 0.03401052620104089,\n\ \ \"acc_norm\": 0.7309941520467836,\n \"acc_norm_stderr\": 0.03401052620104089\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.38555691554467564,\n\ \ \"mc1_stderr\": 0.017038839010591673,\n \"mc2\": 0.5560955080575972,\n\ \ \"mc2_stderr\": 0.014788612119825833\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7387529597474349,\n \"acc_stderr\": 0.012346914863415314\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3009855951478393,\n \ \ \"acc_stderr\": 0.0126345044652112\n }\n}\n```" repo_url: https://huggingface.co/steve-cse/MelloGPT 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_16T18_07_59.704375 path: - '**/details_harness|arc:challenge|25_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-16T18-07-59.704375.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|gsm8k|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hellaswag|10_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T18-07-59.704375.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T18-07-59.704375.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T18-07-59.704375.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T18_07_59.704375 path: - '**/details_harness|winogrande|5_2023-12-16T18-07-59.704375.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-16T18-07-59.704375.parquet' - config_name: results data_files: - split: 2023_12_16T18_07_59.704375 path: - results_2023-12-16T18-07-59.704375.parquet - split: latest path: - results_2023-12-16T18-07-59.704375.parquet --- # Dataset Card for Evaluation run of steve-cse/MelloGPT  Dataset automatically created during the evaluation run of model [steve-cse/MelloGPT](https://huggingface.co/steve-cse/MelloGPT) 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_steve-cse__MelloGPT", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T18:07:59.704375](https://huggingface.co/datasets/open-llm-leaderboard/details_steve-cse__MelloGPT/blob/main/results_2023-12-16T18-07-59.704375.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": 0.5580941639624716, "acc_stderr": 0.03400113351875304, "acc_norm": 0.5629530798562845, "acc_norm_stderr": 0.03471812426856797, "mc1": 0.38555691554467564, "mc1_stderr": 0.017038839010591673, "mc2": 0.5560955080575972, "mc2_stderr": 0.014788612119825833 }, "harness|arc:challenge|25": { "acc": 0.5247440273037542, "acc_stderr": 0.014593487694937738, "acc_norm": 0.53839590443686, "acc_norm_stderr": 0.01456824555029636 }, "harness|hellaswag|10": { "acc": 0.5646285600477993, "acc_stderr": 0.004947922692688834, "acc_norm": 0.761202947619996, "acc_norm_stderr": 0.004254771367531346 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4074074074074074, "acc_stderr": 0.04244633238353229, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.04244633238353229 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5460526315789473, "acc_stderr": 0.04051646342874142, "acc_norm": 0.5460526315789473, "acc_norm_stderr": 0.04051646342874142 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6113207547169811, "acc_stderr": 0.030000485448675986, "acc_norm": 0.6113207547169811, "acc_norm_stderr": 0.030000485448675986 }, "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": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5549132947976878, "acc_stderr": 0.03789401760283648, "acc_norm": 0.5549132947976878, "acc_norm_stderr": 0.03789401760283648 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.045766654032077636, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.045766654032077636 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.48936170212765956, "acc_stderr": 0.03267862331014063, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.03267862331014063 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3508771929824561, "acc_stderr": 0.04489539350270699, "acc_norm": 0.3508771929824561, "acc_norm_stderr": 0.04489539350270699 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155254, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155254 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.36507936507936506, "acc_stderr": 0.043062412591271526, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.043062412591271526 }, "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": 0.6548387096774193, "acc_stderr": 0.02704574657353433, "acc_norm": 0.6548387096774193, "acc_norm_stderr": 0.02704574657353433 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.43349753694581283, "acc_stderr": 0.03486731727419872, "acc_norm": 0.43349753694581283, "acc_norm_stderr": 0.03486731727419872 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6727272727272727, "acc_stderr": 0.03663974994391244, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.03663974994391244 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7272727272727273, "acc_stderr": 0.03173071239071724, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.03173071239071724 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7409326424870466, "acc_stderr": 0.031618779179354115, "acc_norm": 0.7409326424870466, "acc_norm_stderr": 0.031618779179354115 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5307692307692308, "acc_stderr": 0.025302958890850154, "acc_norm": 0.5307692307692308, "acc_norm_stderr": 0.025302958890850154 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815635, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815635 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5630252100840336, "acc_stderr": 0.03221943636566196, "acc_norm": 0.5630252100840336, "acc_norm_stderr": 0.03221943636566196 }, "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": 0.7174311926605504, "acc_stderr": 0.019304243497707152, "acc_norm": 0.7174311926605504, "acc_norm_stderr": 0.019304243497707152 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.48148148148148145, "acc_stderr": 0.03407632093854052, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.03407632093854052 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6764705882352942, "acc_stderr": 0.032834720561085606, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.032834720561085606 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.679324894514768, "acc_stderr": 0.0303819319499904, "acc_norm": 0.679324894514768, "acc_norm_stderr": 0.0303819319499904 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6367713004484304, "acc_stderr": 0.032277904428505, "acc_norm": 0.6367713004484304, "acc_norm_stderr": 0.032277904428505 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7251908396946565, "acc_stderr": 0.03915345408847834, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.03915345408847834 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6363636363636364, "acc_stderr": 0.043913262867240704, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.043913262867240704 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6851851851851852, "acc_stderr": 0.04489931073591312, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.04489931073591312 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6809815950920245, "acc_stderr": 0.03661997551073836, "acc_norm": 0.6809815950920245, "acc_norm_stderr": 0.03661997551073836 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489122, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489122 }, "harness|hendrycksTest-management|5": { "acc": 0.6699029126213593, "acc_stderr": 0.04656147110012351, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.04656147110012351 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077788, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077788 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.67, "acc_stderr": 0.047258156262526094, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526094 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7471264367816092, "acc_stderr": 0.015543377313719681, "acc_norm": 0.7471264367816092, "acc_norm_stderr": 0.015543377313719681 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5982658959537572, "acc_stderr": 0.026394104177643637, "acc_norm": 0.5982658959537572, "acc_norm_stderr": 0.026394104177643637 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.25251396648044694, "acc_stderr": 0.014530330201468647, "acc_norm": 0.25251396648044694, "acc_norm_stderr": 0.014530330201468647 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6405228758169934, "acc_stderr": 0.027475969910660952, "acc_norm": 0.6405228758169934, "acc_norm_stderr": 0.027475969910660952 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6141479099678456, "acc_stderr": 0.027648149599751464, "acc_norm": 0.6141479099678456, "acc_norm_stderr": 0.027648149599751464 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5925925925925926, "acc_stderr": 0.027339546640662734, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.027339546640662734 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.375886524822695, "acc_stderr": 0.028893955412115882, "acc_norm": 0.375886524822695, "acc_norm_stderr": 0.028893955412115882 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.39765319426336376, "acc_stderr": 0.012499840347460642, "acc_norm": 0.39765319426336376, "acc_norm_stderr": 0.012499840347460642 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5588235294117647, "acc_stderr": 0.030161911930767102, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.030161911930767102 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5277777777777778, "acc_stderr": 0.020196594933541194, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.020196594933541194 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6530612244897959, "acc_stderr": 0.030472526026726492, "acc_norm": 0.6530612244897959, "acc_norm_stderr": 0.030472526026726492 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7611940298507462, "acc_stderr": 0.03014777593540922, "acc_norm": 0.7611940298507462, "acc_norm_stderr": 0.03014777593540922 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.5, "acc_stderr": 0.03892494720807614, "acc_norm": 0.5, "acc_norm_stderr": 0.03892494720807614 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7309941520467836, "acc_stderr": 0.03401052620104089, "acc_norm": 0.7309941520467836, "acc_norm_stderr": 0.03401052620104089 }, "harness|truthfulqa:mc|0": { "mc1": 0.38555691554467564, "mc1_stderr": 0.017038839010591673, "mc2": 0.5560955080575972, "mc2_stderr": 0.014788612119825833 }, "harness|winogrande|5": { "acc": 0.7387529597474349, "acc_stderr": 0.012346914863415314 }, "harness|gsm8k|5": { "acc": 0.3009855951478393, "acc_stderr": 0.0126345044652112 } } ``` ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

diogofouto/dialogsum-augmented

--- license: apache-2.0 --- # DialogSum Enhanced Dataset ## Overview DialogSum Enhanced is an extension of the original DialogSum dataset, enriched with a new column called 'Relevant Sentences.' This dataset is designed to facilitate research in dialogue summarization by providing additional information about the dialogue turns that GPT-4 considers relevant for generating summaries. ### Changes from DialogSum The primary enhancement in DialogSum Enhanced is the inclusion of the 'Relevant Sentences' column. This column contains the dialogue turns that GPT-4 identified as crucial for the generation of a summary. This information can be valuable for understanding the model's decision-making process and improving dialogue summarization models. ### Split Information - **Train Split:** The train split in DialogSum Enhanced consists of half of the original DialogSum train split. - **Test and Validation Sets:** The test and validation sets in DialogSum Enhanced retain their full length from the original DialogSum dataset. ## Dataset Structure The dataset is provided in a CSV format with the following columns: 1. **id:** Unique identifier for each dialogue. 2. **dialogue:** The sequential turns of the dialogue. 3. **relevant_sentences:** The dialogue turns that GPT-4 considered relevant for generating the summary. 4. **summary:** The reference summary for the dialogue. ## Usage Researchers and practitioners interested in dialogue summarization can leverage DialogSum Enhanced for training, validating, and testing their models. The 'Relevant Sentences' column provides additional insights into the model's decision-making process during summarization.

atifss/gpt2-124M-qlora-chat-support

--- dataset_info: features: - name: answer dtype: string - name: question dtype: string splits: - name: train num_bytes: 17924 num_examples: 79 download_size: 9896 dataset_size: 17924 --- # Dataset Card for "gpt2-124M-qlora-chat-support" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1

--- pretty_name: Evaluation run of ZoidBB/unraveled-7b-a1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ZoidBB/unraveled-7b-a1](https://huggingface.co/ZoidBB/unraveled-7b-a1) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-23T19:22:53.071269](https://huggingface.co/datasets/open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1_public/blob/main/results_2023-11-23T19-22-53.071269.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\": 0.627016601295214,\n\ \ \"acc_stderr\": 0.03235619334922418,\n \"acc_norm\": 0.6365794887378388,\n\ \ \"acc_norm_stderr\": 0.03306927475699416,\n \"mc1\": 0.28151774785801714,\n\ \ \"mc1_stderr\": 0.01574402724825605,\n \"mc2\": 0.42228384526614654,\n\ \ \"mc2_stderr\": 0.014152177395393957,\n \"em\": 0.0017827181208053692,\n\ \ \"em_stderr\": 0.00043200973460387867,\n \"f1\": 0.06056837248322149,\n\ \ \"f1_stderr\": 0.0013671084143061485\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5648464163822525,\n \"acc_stderr\": 0.014487986197186045,\n\ \ \"acc_norm\": 0.5981228668941979,\n \"acc_norm_stderr\": 0.014327268614578274\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.635929097789285,\n\ \ \"acc_stderr\": 0.004801852881329739,\n \"acc_norm\": 0.8280223063134834,\n\ \ \"acc_norm_stderr\": 0.0037658983649388736\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.04244633238353227\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.039105257528497236,\n\ \ \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.039105257528497236\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\ \ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\ \ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5862068965517241,\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\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\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\"\ : 0.7580645161290323,\n \"acc_stderr\": 0.024362599693031096,\n \"\ acc_norm\": 0.7580645161290323,\n \"acc_norm_stderr\": 0.024362599693031096\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4827586206896552,\n \"acc_stderr\": 0.035158955511657,\n \"acc_norm\"\ : 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511657\n },\n\ \ \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\"\ : 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026704,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026704\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206865,\n \ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206865\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.039439666991836285,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.039439666991836285\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8073394495412844,\n \"acc_stderr\": 0.016909276884936073,\n \"\ acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.016909276884936073\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5787037037037037,\n \"acc_stderr\": 0.033674621388960775,\n \"\ acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.033674621388960775\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.0286265479124374,\n \"acc_norm\"\ : 0.7892156862745098,\n \"acc_norm_stderr\": 0.0286265479124374\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676173,\n \"\ acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676173\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\ \ \"acc_stderr\": 0.04373313040914761,\n \"acc_norm\": 0.7129629629629629,\n\ \ \"acc_norm_stderr\": 0.04373313040914761\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.03157065078911901,\n\ \ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.03157065078911901\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\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092368,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092368\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.013890862162876163,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.013890862162876163\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.708092485549133,\n \"acc_stderr\": 0.024476994076247333,\n\ \ \"acc_norm\": 0.708092485549133,\n \"acc_norm_stderr\": 0.024476994076247333\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3039106145251397,\n\ \ \"acc_stderr\": 0.015382845587584517,\n \"acc_norm\": 0.3039106145251397,\n\ \ \"acc_norm_stderr\": 0.015382845587584517\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n\ \ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\ \ \"acc_stderr\": 0.026385273703464485,\n \"acc_norm\": 0.684887459807074,\n\ \ \"acc_norm_stderr\": 0.026385273703464485\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.02517104191530968,\n\ \ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.02517104191530968\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303062,\n \ \ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303062\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4426336375488918,\n\ \ \"acc_stderr\": 0.012685906538206247,\n \"acc_norm\": 0.4426336375488918,\n\ \ \"acc_norm_stderr\": 0.012685906538206247\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6437908496732027,\n \"acc_stderr\": 0.0193733324207245,\n \ \ \"acc_norm\": 0.6437908496732027,\n \"acc_norm_stderr\": 0.0193733324207245\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.28151774785801714,\n\ \ \"mc1_stderr\": 0.01574402724825605,\n \"mc2\": 0.42228384526614654,\n\ \ \"mc2_stderr\": 0.014152177395393957\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7719021310181531,\n \"acc_stderr\": 0.011793015817663592\n\ \ },\n \"harness|drop|3\": {\n \"em\": 0.0017827181208053692,\n \ \ \"em_stderr\": 0.00043200973460387867,\n \"f1\": 0.06056837248322149,\n\ \ \"f1_stderr\": 0.0013671084143061485\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.14329037149355572,\n \"acc_stderr\": 0.009650895723357585\n\ \ }\n}\n```" repo_url: https://huggingface.co/ZoidBB/unraveled-7b-a1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|arc:challenge|25_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-11-23T19-22-53.071269.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|drop|3_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-23T19-22-53.071269.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|gsm8k|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hellaswag|10_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-23T19-22-53.071269.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-management|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-virology|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-23T19-22-53.071269.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|truthfulqa:mc|0_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-11-23T19-22-53.071269.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_23T19_22_53.071269 path: - '**/details_harness|winogrande|5_2023-11-23T19-22-53.071269.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-23T19-22-53.071269.parquet' - config_name: results data_files: - split: 2023_11_23T19_22_53.071269 path: - results_2023-11-23T19-22-53.071269.parquet - split: latest path: - results_2023-11-23T19-22-53.071269.parquet --- # Dataset Card for Evaluation run of ZoidBB/unraveled-7b-a1 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/ZoidBB/unraveled-7b-a1 - **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 [ZoidBB/unraveled-7b-a1](https://huggingface.co/ZoidBB/unraveled-7b-a1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-23T19:22:53.071269](https://huggingface.co/datasets/open-llm-leaderboard/details_ZoidBB__unraveled-7b-a1_public/blob/main/results_2023-11-23T19-22-53.071269.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": 0.627016601295214, "acc_stderr": 0.03235619334922418, "acc_norm": 0.6365794887378388, "acc_norm_stderr": 0.03306927475699416, "mc1": 0.28151774785801714, "mc1_stderr": 0.01574402724825605, "mc2": 0.42228384526614654, "mc2_stderr": 0.014152177395393957, "em": 0.0017827181208053692, "em_stderr": 0.00043200973460387867, "f1": 0.06056837248322149, "f1_stderr": 0.0013671084143061485 }, "harness|arc:challenge|25": { "acc": 0.5648464163822525, "acc_stderr": 0.014487986197186045, "acc_norm": 0.5981228668941979, "acc_norm_stderr": 0.014327268614578274 }, "harness|hellaswag|10": { "acc": 0.635929097789285, "acc_stderr": 0.004801852881329739, "acc_norm": 0.8280223063134834, "acc_norm_stderr": 0.0037658983649388736 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.04244633238353227, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.04244633238353227 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6381578947368421, "acc_stderr": 0.039105257528497236, "acc_norm": 0.6381578947368421, "acc_norm_stderr": 0.039105257528497236 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566017, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566017 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7580645161290323, "acc_stderr": 0.024362599693031096, "acc_norm": 0.7580645161290323, "acc_norm_stderr": 0.024362599693031096 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511657, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511657 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.02886977846026704, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026704 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758733, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758733 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.362962962962963, "acc_stderr": 0.029318203645206865, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.029318203645206865 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.634453781512605, "acc_stderr": 0.031282177063684614, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.031282177063684614 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.039439666991836285, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.039439666991836285 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8073394495412844, "acc_stderr": 0.016909276884936073, "acc_norm": 0.8073394495412844, "acc_norm_stderr": 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{ "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7129629629629629, "acc_stderr": 0.04373313040914761, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.04373313040914761 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7975460122699386, "acc_stderr": 0.03157065078911901, "acc_norm": 0.7975460122699386, "acc_norm_stderr": 0.03157065078911901 }, "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": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092368, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092368 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8148148148148148, "acc_stderr": 0.013890862162876163, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.013890862162876163 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.708092485549133, "acc_stderr": 0.024476994076247333, "acc_norm": 0.708092485549133, "acc_norm_stderr": 0.024476994076247333 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3039106145251397, "acc_stderr": 0.015382845587584517, "acc_norm": 0.3039106145251397, "acc_norm_stderr": 0.015382845587584517 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7450980392156863, "acc_stderr": 0.02495418432487991, "acc_norm": 0.7450980392156863, "acc_norm_stderr": 0.02495418432487991 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.684887459807074, "acc_stderr": 0.026385273703464485, "acc_norm": 0.684887459807074, "acc_norm_stderr": 0.026385273703464485 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7129629629629629, "acc_stderr": 0.02517104191530968, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.02517104191530968 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4716312056737589, "acc_stderr": 0.029779450957303062, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.029779450957303062 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4426336375488918, "acc_stderr": 0.012685906538206247, "acc_norm": 0.4426336375488918, "acc_norm_stderr": 0.012685906538206247 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462923, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462923 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6437908496732027, "acc_stderr": 0.0193733324207245, "acc_norm": 0.6437908496732027, "acc_norm_stderr": 0.0193733324207245 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482706, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482706 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness|truthfulqa:mc|0": { "mc1": 0.28151774785801714, "mc1_stderr": 0.01574402724825605, "mc2": 0.42228384526614654, "mc2_stderr": 0.014152177395393957 }, "harness|winogrande|5": { "acc": 0.7719021310181531, "acc_stderr": 0.011793015817663592 }, "harness|drop|3": { "em": 0.0017827181208053692, "em_stderr": 0.00043200973460387867, "f1": 0.06056837248322149, "f1_stderr": 0.0013671084143061485 }, "harness|gsm8k|5": { "acc": 0.14329037149355572, "acc_stderr": 0.009650895723357585 } } ``` ### 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]

WangX0111/MLIR-Passes

--- license: mit --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MLIR file associated passes, crawled from the official LLVM/MLIR repository. ### Supported Tasks and Leaderboards Code LLM for MLIR optimization ### Languages MLIR

Oivalf23/jhony

--- license: openrail ---

flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - multilingual pretty_name: stackexchange size_categories: - unknown source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa --- # Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers)s - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [stackexchange](https://archive.org/details/stackexchange) - **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings) ### Dataset Summary We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites). ### Languages Stack Exchange mainly consist of english language (en). ## Dataset Structure ### Data Instances Each data samples is presented as follow: ``` {'title_body': 'How to determine if 3 points on a 3-D graph are collinear? Let the points $A, B$ and $C$ be $(x_1, y_1, z_1), (x_2, y_2, z_2)$ and $(x_3, y_3, z_3)$ respectively. How do I prove that the 3 points are collinear? What is the formula?', 'upvoted_answer': 'From $A(x_1,y_1,z_1),B(x_2,y_2,z_2),C(x_3,y_3,z_3)$ we can get their position vectors.\n\n$\\vec{AB}=(x_2-x_1,y_2-y_1,z_2-z_1)$ and $\\vec{AC}=(x_3-x_1,y_3-y_1,z_3-z_1)$.\n\nThen $||\\vec{AB}\\times\\vec{AC}||=0\\implies A,B,C$ collinear.', ``` This particular exampe corresponds to the [following page](https://math.stackexchange.com/questions/947555/how-to-determine-if-3-points-on-a-3-d-graph-are-collinear) ### Data Fields The fields present in the dataset contain the following informations: - `title_body`: This is the concatenation of the title and body from the question - `upvoted_answer`: This is the body from the most upvoted answer ### Data Splits We provide multiple splits for this dataset, which each refers to a given community channel. We detail the number of pail for each split below: | | Number of pairs | | ----- | ------ | | apple | 92,487 | | english | 100,640 | | codereview | 41,748 | | dba | 71,449 | | mathoverflow | 85,289 | | electronics | 129,494 | | mathematica | 59,895 | | drupal | 67,817 | | magento | 79,241 | | gaming | 82,887 | | ell | 77,892 | | gamedev | 40,154 | | gis | 100,254 | | askubuntu | 267,135 | | diy | 52,896 | | academia | 32,137 | | blender | 54,153 | | cs | 30,010 | | chemistry | 27,061 | | judaism | 26,085 | | crypto | 19,404 | | android | 38,077 | | ja | 17,376 | | christianity | 11,498 | | graphicdesign | 28,083 | | aviation | 18,755 | | ethereum | 26,124 | | biology | 19,277 | | datascience | 20,503 | | law | 16,133 | | dsp | 17,430 | | japanese | 20,948 | | hermeneutics | 9,516 | | bicycles | 15,708 | | arduino | 16,281 | | history | 10,766 | | bitcoin | 22,474 | | cooking | 22,641 | | hinduism | 8,999 | | codegolf | 8,211 | | boardgames | 11,805 | | emacs | 16,830 | | economics | 8,844 | | gardening | 13,246 | | astronomy | 9,086 | | islam | 10,052 | | german | 13,733 | | fitness | 8,297 | | french | 10,578 | | anime | 10,131 | | craftcms | 11,236 | | cstheory | 7,742 | | engineering | 8,649 | | buddhism | 6,787 | | linguistics | 6,843 | | ai | 5,763 | | expressionengine | 10,742 | | cogsci | 5,101 | | chinese | 8,646 | | chess | 6,392 | | civicrm | 10,648 | | literature | 3,539 | | interpersonal | 3,398 | | health | 4,494 | | avp | 6,450 | | earthscience | 4,396 | | joomla | 5,887 | | homebrew | 5,608 | | expatriates | 4,913 | | latin | 3,969 | | matheducators | 2,706 | | ham | 3,501 | | genealogy | 2,895 | | 3dprinting | 3,488 | | elementaryos | 5,917 | | bioinformatics | 3,135 | | devops | 3,462 | | hsm | 2,517 | | italian | 3,101 | | computergraphics | 2,306 | | martialarts | 1,737 | | bricks | 3,530 | | freelancing | 1,663 | | crafts | 1,659 | | lifehacks | 2,576 | | cseducators | 902 | | materials | 1,101 | | hardwarerecs | 2,050 | | iot | 1,359 | | eosio | 1,940 | | languagelearning | 948 | | korean | 1,406 | | coffee | 1,188 | | esperanto | 1,466 | | beer | 1,012 | | ebooks | 1,107 | | iota | 775 | | cardano | 248 | | drones | 496 | | conlang | 334 | | pt | 103,277 | | stats | 115,679 | | unix | 155,414 | | physics | 141,230 | | tex | 171,628 | | serverfault | 238,507 | | salesforce | 87,272 | | wordpress | 83,621 | | softwareengineering | 51,326 | | scifi | 54,805 | | security | 51,355 | | ru | 253,289 | | superuser | 352,610 | | sharepoint | 80,420 | | rpg | 40,435 | | travel | 36,533 | | worldbuilding | 26,210 | | meta | 1,000 | | workplace | 24,012 | | ux | 28,901 | | money | 29,404 | | webmasters | 30,370 | | raspberrypi | 24,143 | | photo | 23,204 | | music | 19,936 | | philosophy | 13,114 | | puzzling | 17,448 | | movies | 18,243 | | quant | 12,933 | | politics | 11,047 | | space | 12,893 | | mechanics | 18,613 | | skeptics | 8,145 | | rus | 16,528 | | writers | 9,867 | | webapps | 24,867 | | softwarerecs | 11,761 | | networkengineering | 12,590 | | parenting | 5,998 | | scicomp | 7,036 | | sqa | 9,256 | | sitecore | 7,838 | | vi | 9,000 | | spanish | 7,675 | | pm | 5,435 | | pets | 6,156 | | sound | 8,303 | | reverseengineering | 5,817 | | outdoors | 5,278 | | tridion | 5,907 | | retrocomputing | 3,907 | | robotics | 4,648 | | quantumcomputing | 4,320 | | sports | 4,707 | | russian | 3,937 | | opensource | 3,221 | | woodworking | 2,955 | | patents | 3,573 | | tor | 4,167 | | ukrainian | 1,767 | | opendata | 3,842 | | monero | 3,508 | | sustainability | 1,674 | | portuguese | 1,964 | | mythology | 1,595 | | musicfans | 2,431 | | or | 1,490 | | poker | 1,665 | | windowsphone | 2,807 | | moderators | 504 | | stackapps | 1,518 | | stellar | 1,078 | | vegetarianism | 585 | | tezos | 1,169 | | total | 4,750,619 | ## Dataset Creation ### Curation Rationale We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically. ### Source Data The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange) #### Initial Data Collection and Normalization We collected the data from the math community. We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters. When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers. #### Who are the source language producers? Questions and answers are written by the community developpers of Stack Exchange. ## Additional Information ### Licensing Information Please see the license information at: https://archive.org/details/stackexchange ### Citation Information ``` @misc{StackExchangeDataset, author = {Flax Sentence Embeddings Team}, title = {Stack Exchange question pairs}, year = {2021}, howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/}, } ``` ### Contributions Thanks to the Flax Sentence Embeddings team for adding this dataset.

allenai/multinews_dense_max

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual pretty_name: Multi-News size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: multi-news train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: document: text summary: target metrics: - type: rouge name: Rouge --- This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __dense__ retriever. The retrieval pipeline used: - __query__: The `summary` field of each example - __corpus__: The union of all documents in the `train`, `validation` and `test` splits - __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings - __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==10` Retrieval results on the `train` set: Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.8661 | 0.6867 | 0.2118 | 0.7966 | Retrieval results on the `validation` set: Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.8626 | 0.6859 | 0.2083 | 0.7949 | Retrieval results on the `test` set: Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.8625 | 0.6927 | 0.2096 | 0.7971 |

distilled-one-sec-cv12-each-chunk-uniq/chunk_68

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1114218784.0 num_examples: 217112 download_size: 1136538595 dataset_size: 1114218784.0 --- # Dataset Card for "chunk_68" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_HWERI__pythia-70m-deduped-cleansharegpt

--- pretty_name: Evaluation run of HWERI/pythia-70m-deduped-cleansharegpt dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [HWERI/pythia-70m-deduped-cleansharegpt](https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt)\ \ 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_HWERI__pythia-70m-deduped-cleansharegpt\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T05:46:53.221413](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__pythia-70m-deduped-cleansharegpt/blob/main/results_2023-10-29T05-46-53.221413.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\": 0.0,\n \"\ em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\ acc\": 0.2600631412786109,\n \"acc_stderr\": 0.007020548332172166\n },\n\ \ \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\ \ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\"\ : {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5201262825572218,\n \"acc_stderr\": 0.014041096664344332\n\ \ }\n}\n```" repo_url: https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|arc:challenge|25_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-13T12-28-53.949092.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T05_46_53.221413 path: - '**/details_harness|drop|3_2023-10-29T05-46-53.221413.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-29T05-46-53.221413.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T05_46_53.221413 path: - '**/details_harness|gsm8k|5_2023-10-29T05-46-53.221413.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-29T05-46-53.221413.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hellaswag|10_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T12-28-53.949092.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T12-28-53.949092.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_13T12_28_53.949092 path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T12-28-53.949092.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T12-28-53.949092.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T05_46_53.221413 path: - '**/details_harness|winogrande|5_2023-10-29T05-46-53.221413.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-29T05-46-53.221413.parquet' - config_name: results data_files: - split: 2023_09_13T12_28_53.949092 path: - results_2023-09-13T12-28-53.949092.parquet - split: 2023_10_29T05_46_53.221413 path: - results_2023-10-29T05-46-53.221413.parquet - split: latest path: - results_2023-10-29T05-46-53.221413.parquet --- # Dataset Card for Evaluation run of HWERI/pythia-70m-deduped-cleansharegpt ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt - **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 [HWERI/pythia-70m-deduped-cleansharegpt](https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt) 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_HWERI__pythia-70m-deduped-cleansharegpt", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T05:46:53.221413](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__pythia-70m-deduped-cleansharegpt/blob/main/results_2023-10-29T05-46-53.221413.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": 0.0, "em_stderr": 0.0, "f1": 0.0, "f1_stderr": 0.0, "acc": 0.2600631412786109, "acc_stderr": 0.007020548332172166 }, "harness|drop|3": { "em": 0.0, "em_stderr": 0.0, "f1": 0.0, "f1_stderr": 0.0 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.5201262825572218, "acc_stderr": 0.014041096664344332 } } ``` ### 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]

miehin/test13aug

--- license: openrail ---

arthurmluz/GPTextSum_data-wiki_gptextsum_results

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 splits: - name: validation num_bytes: 28733 num_examples: 20 download_size: 35161 dataset_size: 28733 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "GPTextSum_data-wiki_gptextsum_results" rouge= {'rouge1': 0.47135937656590865, 'rouge2': 0.25940674061381325, 'rougeL': 0.38504581702889945, 'rougeLsum': 0.38504581702889945} bert= {'precision': 0.788231149315834, 'recall': 0.7805202662944793, 'f1': 0.7840157747268677}

lhallee/abstract_domain_copd

--- dataset_info: features: - name: a dtype: string - name: b dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 477301584 num_examples: 132453 - name: valid num_bytes: 9596971 num_examples: 2676 - name: test num_bytes: 4758204 num_examples: 1294 download_size: 200765538 dataset_size: 491656759 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* --- This dataset contains the cocitation abstracts related to COPD in the paper [Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings](arxiv.org/abs/2401.15713)

AdapterOcean/physics_dataset_standardized_cluster_4_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 7334043 num_examples: 3437 download_size: 0 dataset_size: 7334043 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "physics_dataset_standardized_cluster_4_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pourmand1376/alpaca-fa-multi

--- dataset_info: features: - name: instruction dtype: string - name: conversation dtype: string - name: SOURCE dtype: string splits: - name: train num_bytes: 2694606 num_examples: 3907 download_size: 1227811 dataset_size: 2694606 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - question-answering - text-generation - conversational language: - fa pretty_name: Alpaca Farsi Multi-turn size_categories: - 10K<n<100K --- # Dataset Card for "alpaca-fa-multi" This dataset is first published [here](https://www.kaggle.com/datasets/amirpourmand/alpaca-farsi) and then converted to this style to comply with open-assistant standards. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DataHammer/emotional_dialog

--- license: apache-2.0 task_categories: - text-generation - conversational language: - zh size_categories: - 1K<n<10K --- # Scientific Emotional Dialogue ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This is a dataset for emotional multi-turn dialogue on scientific research personnels. It consists of 1069 dialogues with 2709 turns. The Dialogue was first written by NLP practitioners and then expanded by GPT4. ### Supported Tasks and Leaderboards - Emotional Dialogue: The dataset can be used to instruction tuning for emotional dialogue. ### Languages Chinese ## Dataset Structure ### Data Instances A typical instance in the dataset: ``` [ "Human: 真是郁闷，我的论文被拒了，我觉得自己完全失败了。", "Bot: 嘿，别这么想嘛，论文被拒并不代表你是个失败者。审稿人的意见只是一种反馈，你可以从中学到很多东西。别放弃，修改论文并尝试其他期刊，我相信你能成功的！", "Human: 嗯，或许你说得对。但我还是觉得很失望，觉得自己好像没进步。", "Bot: 失望是正常的情绪，但不要让它打击你。论文写作是一个成长的过程，每次经历都是宝贵的。相信自己的能力，继续努力，你一定能取得进步的！", "Human: 谢谢你的鼓励，我会再试一次的。", "Bot: 太棒了！相信自己，你可以做到的！" ] ```

gracefulibk/yorubamt

--- license: afl-3.0 ---

Asap7772/skewlognormal_maxlength

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: output dtype: string - name: text dtype: string - name: alpaca_text dtype: string - name: prompt dtype: string - name: alpaca_prompt dtype: string - name: y_ref dtype: string - name: y_1 dtype: string - name: y_2 dtype: string - name: y_w dtype: string - name: y_w_alpaca dtype: string - name: y_l dtype: string - name: y_l_alpaca dtype: string - name: y_w_score dtype: float64 - name: y_l_score dtype: float64 - name: score_diff dtype: float64 splits: - name: train num_bytes: 77844991 num_examples: 19000 - name: test num_bytes: 4082779 num_examples: 1000 download_size: 40225253 dataset_size: 81927770 --- # Dataset Card for "skewlognormal_maxlength" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sxdave/emotion_detection

--- task_categories: - image-classification language: - en tags: - happy - sad - neutral size_categories: - n<1K ---

mizunorlk/mizuno

--- license: openrail ---

xaviviro/oasst1_ca

--- dataset_info: features: - name: message_id dtype: string - name: parent_id dtype: string - name: user_id dtype: string - name: created_date dtype: string - name: text dtype: string - name: role dtype: string - name: lang dtype: string - name: review_count dtype: int64 - name: review_result dtype: bool - name: deleted dtype: bool - name: rank dtype: float64 - name: synthetic dtype: bool - name: model_name dtype: 'null' - name: detoxify struct: - name: identity_attack dtype: float64 - name: insult dtype: float64 - name: obscene dtype: float64 - name: severe_toxicity dtype: float64 - name: sexual_explicit dtype: float64 - name: threat dtype: float64 - name: toxicity dtype: float64 - name: message_tree_id dtype: string - name: tree_state dtype: string - name: emojis struct: - name: count sequence: int64 - name: name sequence: string - name: labels struct: - name: count sequence: int64 - name: name sequence: string - name: value sequence: float64 splits: - name: validation num_bytes: 4690639 num_examples: 4399 - name: train num_bytes: 89877756 num_examples: 84398 download_size: 33007102 dataset_size: 94568395 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* language: - ca ---

open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test

--- pretty_name: Evaluation run of lu-vae/llama2-13b-sharegpt4-test dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lu-vae/llama2-13b-sharegpt4-test](https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test)\ \ 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_lu-vae__llama2-13b-sharegpt4-test\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-25T11:44:44.255542](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test/blob/main/results_2023-10-25T11-44-44.255542.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\": 0.002202181208053691,\n\ \ \"em_stderr\": 0.00048005108166192744,\n \"f1\": 0.06610843120805385,\n\ \ \"f1_stderr\": 0.0014639889801036593,\n \"acc\": 0.44600618846762125,\n\ \ \"acc_stderr\": 0.01064352054021588\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.00048005108166192744,\n\ \ \"f1\": 0.06610843120805385,\n \"f1_stderr\": 0.0014639889801036593\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13115996967399546,\n \ \ \"acc_stderr\": 0.00929849923558786\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.760852407261247,\n \"acc_stderr\": 0.011988541844843902\n\ \ }\n}\n```" repo_url: https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|arc:challenge|25_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-12T16-41-26.998548.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_25T11_44_44.255542 path: - '**/details_harness|drop|3_2023-10-25T11-44-44.255542.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-25T11-44-44.255542.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_25T11_44_44.255542 path: - '**/details_harness|gsm8k|5_2023-10-25T11-44-44.255542.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-25T11-44-44.255542.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hellaswag|10_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T16-41-26.998548.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T16-41-26.998548.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T16_41_26.998548 path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T16-41-26.998548.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T16-41-26.998548.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_25T11_44_44.255542 path: - '**/details_harness|winogrande|5_2023-10-25T11-44-44.255542.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-25T11-44-44.255542.parquet' - config_name: results data_files: - split: 2023_09_12T16_41_26.998548 path: - results_2023-09-12T16-41-26.998548.parquet - split: 2023_10_25T11_44_44.255542 path: - results_2023-10-25T11-44-44.255542.parquet - split: latest path: - results_2023-10-25T11-44-44.255542.parquet --- # Dataset Card for Evaluation run of lu-vae/llama2-13b-sharegpt4-test ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test - **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 [lu-vae/llama2-13b-sharegpt4-test](https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test) 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_lu-vae__llama2-13b-sharegpt4-test", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-25T11:44:44.255542](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test/blob/main/results_2023-10-25T11-44-44.255542.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": 0.002202181208053691, "em_stderr": 0.00048005108166192744, "f1": 0.06610843120805385, "f1_stderr": 0.0014639889801036593, "acc": 0.44600618846762125, "acc_stderr": 0.01064352054021588 }, "harness|drop|3": { "em": 0.002202181208053691, "em_stderr": 0.00048005108166192744, "f1": 0.06610843120805385, "f1_stderr": 0.0014639889801036593 }, "harness|gsm8k|5": { "acc": 0.13115996967399546, "acc_stderr": 0.00929849923558786 }, "harness|winogrande|5": { "acc": 0.760852407261247, "acc_stderr": 0.011988541844843902 } } ``` ### 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]

ravithejads/alpaca_urdu_cleaned_input

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: telugu_instruction dtype: string - name: telugu_input dtype: string - name: telugu_output dtype: string - name: telugu_transliterated_instruction dtype: string - name: telugu_transliterated_input dtype: string - name: telugu_transliterated_output dtype: string - name: urdu_instruction dtype: string - name: urdu_input dtype: string splits: - name: train num_bytes: 148976621 num_examples: 28910 download_size: 67681432 dataset_size: 148976621 configs: - config_name: default data_files: - split: train path: data/train-* ---

dhuynh95/Magicoder-Evol-Instruct-110K-Filtered_0.35-text

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 10765290 num_examples: 5000 download_size: 5317750 dataset_size: 10765290 configs: - config_name: default data_files: - split: train path: data/train-* ---

AlekseyKorshuk/intothatgoodnight-guanaco-chatml

--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: do_train dtype: bool - name: role dtype: string splits: - name: train num_bytes: 69125408 num_examples: 50000 download_size: 38037513 dataset_size: 69125408 --- # Dataset Card for "intothatgoodnight-guanaco-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tyzhu/squad_no_rare_v4_train_30_eval_10_recite_ans_sent

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 581992 num_examples: 368 - name: validation num_bytes: 48145 num_examples: 50 download_size: 112955 dataset_size: 630137 --- # Dataset Card for "squad_no_rare_v4_train_30_eval_10_recite_ans_sent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

OpenPipe/hacker-news

--- dataset_info: features: - name: id dtype: int64 - name: type dtype: string - name: by dtype: string - name: time dtype: timestamp[us] - name: title dtype: string - name: text dtype: string - name: url dtype: string - name: score dtype: float64 - name: parent dtype: float64 - name: top_level_parent dtype: int64 - name: descendants dtype: float64 - name: kids sequence: int64 - name: deleted dtype: bool - name: dead dtype: bool splits: - name: train num_bytes: 16886975696 num_examples: 38109500 download_size: 9948795138 dataset_size: 16886975696 configs: - config_name: default data_files: - split: train path: data/train-* --- # Hacker News posts and comments This is a dataset of all HN posts and comments, current as of November 1, 2023.

hamza50/testimg

--- license: wtfpl ---

bagusatmaja/securityobjectives

--- language: - en ---

zz545906747/blue_eye_dragon

--- license: apache-2.0 ---

open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3

--- pretty_name: Evaluation run of shaohang/Sparse0.5_OPT-1.3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [shaohang/Sparse0.5_OPT-1.3](https://huggingface.co/shaohang/Sparse0.5_OPT-1.3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-16T21:48:19.303713](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3/blob/main/results_2023-09-16T21-48-19.303713.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\": 0.003145973154362416,\n\ \ \"em_stderr\": 0.0005734993648436398,\n \"f1\": 0.047173867449664536,\n\ \ \"f1_stderr\": 0.0012666649528854216,\n \"acc\": 0.29319675461487227,\n\ \ \"acc_stderr\": 0.007301498172995543\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436398,\n\ \ \"f1\": 0.047173867449664536,\n \"f1_stderr\": 0.0012666649528854216\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \ \ \"acc_stderr\": 0.0007581501137225237\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.585635359116022,\n \"acc_stderr\": 0.013844846232268563\n\ \ }\n}\n```" repo_url: https://huggingface.co/shaohang/Sparse0.5_OPT-1.3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_09_16T21_48_19.303713 path: - '**/details_harness|drop|3_2023-09-16T21-48-19.303713.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-16T21-48-19.303713.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_16T21_48_19.303713 path: - '**/details_harness|gsm8k|5_2023-09-16T21-48-19.303713.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-16T21-48-19.303713.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_16T21_48_19.303713 path: - '**/details_harness|winogrande|5_2023-09-16T21-48-19.303713.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-16T21-48-19.303713.parquet' - config_name: results data_files: - split: 2023_09_16T21_48_19.303713 path: - results_2023-09-16T21-48-19.303713.parquet - split: latest path: - results_2023-09-16T21-48-19.303713.parquet --- # Dataset Card for Evaluation run of shaohang/Sparse0.5_OPT-1.3 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/shaohang/Sparse0.5_OPT-1.3 - **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 [shaohang/Sparse0.5_OPT-1.3](https://huggingface.co/shaohang/Sparse0.5_OPT-1.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-16T21:48:19.303713](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__Sparse0.5_OPT-1.3/blob/main/results_2023-09-16T21-48-19.303713.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": 0.003145973154362416, "em_stderr": 0.0005734993648436398, "f1": 0.047173867449664536, "f1_stderr": 0.0012666649528854216, "acc": 0.29319675461487227, "acc_stderr": 0.007301498172995543 }, "harness|drop|3": { "em": 0.003145973154362416, "em_stderr": 0.0005734993648436398, "f1": 0.047173867449664536, "f1_stderr": 0.0012666649528854216 }, "harness|gsm8k|5": { "acc": 0.000758150113722517, "acc_stderr": 0.0007581501137225237 }, "harness|winogrande|5": { "acc": 0.585635359116022, "acc_stderr": 0.013844846232268563 } } ``` ### 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]

aashay96/translated-dataset-synthetic-retrieval-tasks

--- license: apache-2.0 ---

open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1

--- pretty_name: Evaluation run of mncai/mistral-7b-dpo-merge-v1.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mncai/mistral-7b-dpo-merge-v1.1](https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_mncai__mistral-7b-dpo-merge-v1.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-18T08:24:07.649575](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1/blob/main/results_2023-12-18T08-24-07.649575.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\": 0.653910016295201,\n\ \ \"acc_stderr\": 0.03202751255847507,\n \"acc_norm\": 0.6535699044300431,\n\ \ \"acc_norm_stderr\": 0.03269009606304505,\n \"mc1\": 0.5520195838433293,\n\ \ \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6847598329649042,\n\ \ \"mc2_stderr\": 0.015228501222479747\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6945392491467577,\n \"acc_stderr\": 0.013460080478002503,\n\ \ \"acc_norm\": 0.7252559726962458,\n \"acc_norm_stderr\": 0.013044617212771227\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7080262895837482,\n\ \ \"acc_stderr\": 0.004537410615572944,\n \"acc_norm\": 0.8814977096195977,\n\ \ \"acc_norm_stderr\": 0.0032254141192897138\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724053,\n\ \ \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724053\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\ \ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.030956636328566545,\n\ \ \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.030956636328566545\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.034076320938540516,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.034076320938540516\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\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\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371802\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069356,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069356\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4770949720670391,\n\ \ \"acc_stderr\": 0.016704945740326188,\n \"acc_norm\": 0.4770949720670391,\n\ \ \"acc_norm_stderr\": 0.016704945740326188\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\ \ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4716312056737589,\n \"acc_stderr\": 0.02977945095730307,\n \ \ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.02977945095730307\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4706649282920469,\n\ \ \"acc_stderr\": 0.012748238397365549,\n \"acc_norm\": 0.4706649282920469,\n\ \ \"acc_norm_stderr\": 0.012748238397365549\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n\ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724553,\n \ \ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724553\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\": 0.7346938775510204,\n \"acc_stderr\": 0.02826388994378459,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.02826388994378459\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5520195838433293,\n\ \ \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6847598329649042,\n\ \ \"mc2_stderr\": 0.015228501222479747\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8232044198895028,\n \"acc_stderr\": 0.010721923287918753\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7088703563305534,\n \ \ \"acc_stderr\": 0.012513215297888463\n }\n}\n```" repo_url: https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1 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: 2023_12_18T08_24_07.649575 path: - '**/details_harness|arc:challenge|25_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-18T08-24-07.649575.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|gsm8k|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hellaswag|10_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-24-07.649575.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-management|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-24-07.649575.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|truthfulqa:mc|0_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-18T08-24-07.649575.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_18T08_24_07.649575 path: - '**/details_harness|winogrande|5_2023-12-18T08-24-07.649575.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-18T08-24-07.649575.parquet' - config_name: results data_files: - split: 2023_12_18T08_24_07.649575 path: - results_2023-12-18T08-24-07.649575.parquet - split: latest path: - results_2023-12-18T08-24-07.649575.parquet --- # Dataset Card for Evaluation run of mncai/mistral-7b-dpo-merge-v1.1  Dataset automatically created during the evaluation run of model [mncai/mistral-7b-dpo-merge-v1.1](https://huggingface.co/mncai/mistral-7b-dpo-merge-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_mncai__mistral-7b-dpo-merge-v1.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T08:24:07.649575](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__mistral-7b-dpo-merge-v1.1/blob/main/results_2023-12-18T08-24-07.649575.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": 0.653910016295201, "acc_stderr": 0.03202751255847507, "acc_norm": 0.6535699044300431, "acc_norm_stderr": 0.03269009606304505, "mc1": 0.5520195838433293, "mc1_stderr": 0.017408513063422917, "mc2": 0.6847598329649042, "mc2_stderr": 0.015228501222479747 }, "harness|arc:challenge|25": { "acc": 0.6945392491467577, "acc_stderr": 0.013460080478002503, "acc_norm": 0.7252559726962458, "acc_norm_stderr": 0.013044617212771227 }, "harness|hellaswag|10": { "acc": 0.7080262895837482, "acc_stderr": 0.004537410615572944, "acc_norm": 0.8814977096195977, "acc_norm_stderr": 0.0032254141192897138 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.037385206761196686, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.037385206761196686 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7245283018867924, "acc_stderr": 0.027495663683724053, "acc_norm": 0.7245283018867924, "acc_norm_stderr": 0.027495663683724053 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.035676037996391706, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.035676037996391706 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406783, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406783 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.029376616484945633, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.029376616484945633 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6512605042016807, "acc_stderr": 0.030956636328566545, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.030956636328566545 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5185185185185185, "acc_stderr": 0.034076320938540516, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.034076320938540516 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "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": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "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": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371802, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371802 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069356, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069356 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4770949720670391, "acc_stderr": 0.016704945740326188, "acc_norm": 0.4770949720670391, "acc_norm_stderr": 0.016704945740326188 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7376543209876543, "acc_stderr": 0.024477222856135114, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.024477222856135114 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4716312056737589, "acc_stderr": 0.02977945095730307, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.02977945095730307 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4706649282920469, "acc_stderr": 0.012748238397365549, "acc_norm": 0.4706649282920469, "acc_norm_stderr": 0.012748238397365549 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6911764705882353, "acc_stderr": 0.02806499816704009, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.02806499816704009 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6699346405228758, "acc_stderr": 0.019023726160724553, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.019023726160724553 }, "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": 0.7346938775510204, "acc_stderr": 0.02826388994378459, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.02826388994378459 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness|truthfulqa:mc|0": { "mc1": 0.5520195838433293, "mc1_stderr": 0.017408513063422917, "mc2": 0.6847598329649042, "mc2_stderr": 0.015228501222479747 }, "harness|winogrande|5": { "acc": 0.8232044198895028, "acc_stderr": 0.010721923287918753 }, "harness|gsm8k|5": { "acc": 0.7088703563305534, "acc_stderr": 0.012513215297888463 } } ``` ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

maxidl/MathInstruct-de

--- dataset_info: features: - name: source dtype: string - name: messages_en list: - name: content dtype: string - name: role dtype: string - name: messages_de list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 87671574 num_examples: 56793 download_size: 43803287 dataset_size: 87671574 configs: - config_name: default data_files: - split: train path: data/train-* --- German version of [TIGER-Lab/MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct). Translated using DeepL (informal style). Currently limited to the first 56_793 examples. |lang|#chars| |---|---| |en|38_793_154| |de|42_851_569|

kannanwisen/Indian-Traffic-Sign-Classification

--- license: cc-by-4.0 ---

ghomasHudson/muld_NarrativeQA

--- dataset_info: features: - name: input dtype: string - name: output sequence: string splits: - name: test num_bytes: 3435452065 num_examples: 10143 - name: train num_bytes: 11253796383 num_examples: 32747 - name: validation num_bytes: 1176625993 num_examples: 3373 download_size: 8819172017 dataset_size: 15865874441 --- # Dataset Card for "muld_NarrativeQA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alirzb/WrongPreds

--- dataset_info: features: - name: array sequence: float64 - name: label_true dtype: int64 - name: label_pred dtype: int64 - name: id dtype: string - name: ws dtype: image splits: - name: train num_bytes: 4330982.0 num_examples: 9 download_size: 1745905 dataset_size: 4330982.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

nielsr/datacomp_small_with_language

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: uid dtype: string - name: url dtype: string - name: text dtype: string - name: original_width dtype: int64 - name: original_height dtype: int64 - name: clip_b32_similarity_score dtype: float32 - name: clip_l14_similarity_score dtype: float32 - name: face_bboxes sequence: sequence: float64 - name: sha256 dtype: string - name: detected_language dtype: string splits: - name: train num_bytes: 4108589578 num_examples: 12800000 download_size: 3339674305 dataset_size: 4108589578 --- # Dataset Card for "datacomp_small_with_language" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pixparse/docvqa-wds

--- license: mit --- # Dataset Card for DocVQA Dataset ## Dataset Description - **Point of Contact from curators:** [Minesh Mathew](mailto:minesh.mathew@gmail.com), [Dimosthenis Karatzas](dimos@cvc.uab.es), [C. V. Jawahar](jawahar@iiit.ac.in) - **Point of Contact Hugging Face:** [Pablo Montalvo](mailto:pablo@huggingface.co) ### Dataset Summary DocVQA dataset is a document dataset introduced in Mathew et al. (2021) consisting of 50,000 questions defined on 12,000+ document images. This is a webdataset (.tar shard) version equivalent to https://huggingface.co/datasets/pixparse/docvqa-single-page-questions in content. Please visit the challenge page (https://rrc.cvc.uab.es/?ch=17) and paper (https://arxiv.org/abs/2007.00398) for further information. ### Usage This dataset can be used with current releases of Hugging Face `datasets` or `chug` libraries. Examples WIP ### Data Splits #### Train * 10194 images, 39463 questions and answers. ### Validation * 1286 images, 5349 questions and answers. ### Test * 1,287 images, 5,188 questions. ## Additional Information ### Dataset Curators For original authors of the dataset, see citation below. Hugging Face points of contact for this instance: Pablo Montalvo, Ross Wightman ### Licensing Information MIT ### Citation Information ```bibtex @InProceedings{docvqa_wacv, author = {Mathew, Minesh and Karatzas, Dimosthenis and Jawahar, C.V.}, title = {DocVQA: A Dataset for VQA on Document Images}, booktitle = {WACV}, year = {2021}, pages = {2200-2209} } ```

sankovic/jotapedataset

--- license: openrail ---

AlekseyKorshuk/product-photography-v1-categorized

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: category dtype: string - name: product dtype: string - name: prompt dtype: string - name: image_mask dtype: image - name: text dtype: string splits: - name: train num_bytes: 6054931158.644 num_examples: 3606 - name: validation num_bytes: 289483345.0 num_examples: 176 download_size: 6341502964 dataset_size: 6344414503.644 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp

--- pretty_name: Evaluation run of allknowingroger/Synthiallamaguanco-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [allknowingroger/Synthiallamaguanco-7B-slerp](https://huggingface.co/allknowingroger/Synthiallamaguanco-7B-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-10T20:19:16.142131](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp/blob/main/results_2024-04-10T20-19-16.142131.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\": 0.5022519883105377,\n\ \ \"acc_stderr\": 0.03435165246376145,\n \"acc_norm\": 0.5073979301587215,\n\ \ \"acc_norm_stderr\": 0.03511097439217402,\n \"mc1\": 0.29865361077111385,\n\ \ \"mc1_stderr\": 0.016021570613768542,\n \"mc2\": 0.455854048100616,\n\ \ \"mc2_stderr\": 0.01483939681310818\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5102389078498294,\n \"acc_stderr\": 0.014608326906285012,\n\ \ \"acc_norm\": 0.5435153583617748,\n \"acc_norm_stderr\": 0.01455594976049644\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5936068512248556,\n\ \ \"acc_stderr\": 0.004901558132335522,\n \"acc_norm\": 0.7909778928500298,\n\ \ \"acc_norm_stderr\": 0.00405779217189357\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\ \ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.45925925925925926,\n\ \ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.48026315789473684,\n \"acc_stderr\": 0.040657710025626036,\n\ \ \"acc_norm\": 0.48026315789473684,\n \"acc_norm_stderr\": 0.040657710025626036\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.5433962264150943,\n \"acc_stderr\": 0.03065674869673943,\n\ \ \"acc_norm\": 0.5433962264150943,\n \"acc_norm_stderr\": 0.03065674869673943\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5138888888888888,\n\ \ \"acc_stderr\": 0.041795966175810016,\n \"acc_norm\": 0.5138888888888888,\n\ \ \"acc_norm_stderr\": 0.041795966175810016\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.41040462427745666,\n\ \ \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.41040462427745666,\n\ \ \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.03257901482099835,\n\ \ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.03257901482099835\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\ \ \"acc_stderr\": 0.04339138322579862,\n \"acc_norm\": 0.30701754385964913,\n\ \ \"acc_norm_stderr\": 0.04339138322579862\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.30158730158730157,\n \"acc_stderr\": 0.023636975996101806,\n \"\ acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.023636975996101806\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\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\": 0.535483870967742,\n\ \ \"acc_stderr\": 0.028372287797962935,\n \"acc_norm\": 0.535483870967742,\n\ \ \"acc_norm_stderr\": 0.028372287797962935\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3793103448275862,\n \"acc_stderr\": 0.03413963805906235,\n\ \ \"acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.03413963805906235\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\"\ : 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6484848484848484,\n \"acc_stderr\": 0.037282069986826503,\n\ \ \"acc_norm\": 0.6484848484848484,\n \"acc_norm_stderr\": 0.037282069986826503\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6212121212121212,\n \"acc_stderr\": 0.03456088731993747,\n \"\ acc_norm\": 0.6212121212121212,\n \"acc_norm_stderr\": 0.03456088731993747\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7305699481865285,\n \"acc_stderr\": 0.03201867122877794,\n\ \ \"acc_norm\": 0.7305699481865285,\n \"acc_norm_stderr\": 0.03201867122877794\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.47435897435897434,\n \"acc_stderr\": 0.025317649726448652,\n\ \ \"acc_norm\": 0.47435897435897434,\n \"acc_norm_stderr\": 0.025317649726448652\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073845,\n \ \ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073845\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.0322529423239964,\n \ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.0322529423239964\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6844036697247706,\n \"acc_stderr\": 0.01992611751386967,\n \"\ acc_norm\": 0.6844036697247706,\n \"acc_norm_stderr\": 0.01992611751386967\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.3333333333333333,\n \"acc_stderr\": 0.03214952147802749,\n \"\ acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.03214952147802749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6862745098039216,\n \"acc_stderr\": 0.03256685484460388,\n \"\ acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.03256685484460388\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7341772151898734,\n \"acc_stderr\": 0.028756799629658342,\n \ \ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6188340807174888,\n\ \ \"acc_stderr\": 0.03259625118416828,\n \"acc_norm\": 0.6188340807174888,\n\ \ \"acc_norm_stderr\": 0.03259625118416828\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\ \ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6033057851239669,\n \"acc_stderr\": 0.044658697805310094,\n \"\ acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.044658697805310094\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5648148148148148,\n\ \ \"acc_stderr\": 0.04792898170907061,\n \"acc_norm\": 0.5648148148148148,\n\ \ \"acc_norm_stderr\": 0.04792898170907061\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5460122699386503,\n \"acc_stderr\": 0.0391170190467718,\n\ \ \"acc_norm\": 0.5460122699386503,\n \"acc_norm_stderr\": 0.0391170190467718\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n\ \ \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.4017857142857143,\n\ \ \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7735042735042735,\n\ \ \"acc_stderr\": 0.027421007295392923,\n \"acc_norm\": 0.7735042735042735,\n\ \ \"acc_norm_stderr\": 0.027421007295392923\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6973180076628352,\n\ \ \"acc_stderr\": 0.016428781581749364,\n \"acc_norm\": 0.6973180076628352,\n\ \ \"acc_norm_stderr\": 0.016428781581749364\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5346820809248555,\n \"acc_stderr\": 0.026854257928258875,\n\ \ \"acc_norm\": 0.5346820809248555,\n \"acc_norm_stderr\": 0.026854257928258875\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767867,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767867\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.545751633986928,\n \"acc_stderr\": 0.02850980780262659,\n\ \ \"acc_norm\": 0.545751633986928,\n \"acc_norm_stderr\": 0.02850980780262659\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6109324758842444,\n\ \ \"acc_stderr\": 0.027690337536485376,\n \"acc_norm\": 0.6109324758842444,\n\ \ \"acc_norm_stderr\": 0.027690337536485376\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.558641975308642,\n \"acc_stderr\": 0.027628737155668777,\n\ \ \"acc_norm\": 0.558641975308642,\n \"acc_norm_stderr\": 0.027628737155668777\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3900709219858156,\n \"acc_stderr\": 0.02909767559946393,\n \ \ \"acc_norm\": 0.3900709219858156,\n \"acc_norm_stderr\": 0.02909767559946393\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3709256844850065,\n\ \ \"acc_stderr\": 0.012337391684530312,\n \"acc_norm\": 0.3709256844850065,\n\ \ \"acc_norm_stderr\": 0.012337391684530312\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5110294117647058,\n \"acc_stderr\": 0.030365446477275675,\n\ \ \"acc_norm\": 0.5110294117647058,\n \"acc_norm_stderr\": 0.030365446477275675\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.020227834851568375,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.020227834851568375\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.5545454545454546,\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\": 0.5673469387755102,\n\ \ \"acc_stderr\": 0.031717528240626645,\n \"acc_norm\": 0.5673469387755102,\n\ \ \"acc_norm_stderr\": 0.031717528240626645\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.6417910447761194,\n \"acc_stderr\": 0.03390393042268814,\n\ \ \"acc_norm\": 0.6417910447761194,\n \"acc_norm_stderr\": 0.03390393042268814\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\": 0.42168674698795183,\n \"acc_stderr\": 0.03844453181770917,\n\ \ \"acc_norm\": 0.42168674698795183,\n \"acc_norm_stderr\": 0.03844453181770917\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.6900584795321637,\n\ \ \"acc_stderr\": 0.035469769593931624,\n \"acc_norm\": 0.6900584795321637,\n\ \ \"acc_norm_stderr\": 0.035469769593931624\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.29865361077111385,\n \"mc1_stderr\": 0.016021570613768542,\n\ \ \"mc2\": 0.455854048100616,\n \"mc2_stderr\": 0.01483939681310818\n\ \ },\n \"harness|winogrande|5\": {\n \"acc\": 0.744277821625888,\n\ \ \"acc_stderr\": 0.012261253845440473\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.18726307808946172,\n \"acc_stderr\": 0.010745914199510815\n\ \ }\n}\n```" repo_url: https://huggingface.co/allknowingroger/Synthiallamaguanco-7B-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|arc:challenge|25_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-10T20-19-16.142131.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|gsm8k|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hellaswag|10_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-10T20-19-16.142131.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-management|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T20-19-16.142131.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|truthfulqa:mc|0_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-10T20-19-16.142131.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_10T20_19_16.142131 path: - '**/details_harness|winogrande|5_2024-04-10T20-19-16.142131.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-10T20-19-16.142131.parquet' - config_name: results data_files: - split: 2024_04_10T20_19_16.142131 path: - results_2024-04-10T20-19-16.142131.parquet - split: latest path: - results_2024-04-10T20-19-16.142131.parquet --- # Dataset Card for Evaluation run of allknowingroger/Synthiallamaguanco-7B-slerp  Dataset automatically created during the evaluation run of model [allknowingroger/Synthiallamaguanco-7B-slerp](https://huggingface.co/allknowingroger/Synthiallamaguanco-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-10T20:19:16.142131](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Synthiallamaguanco-7B-slerp/blob/main/results_2024-04-10T20-19-16.142131.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": 0.5022519883105377, "acc_stderr": 0.03435165246376145, "acc_norm": 0.5073979301587215, "acc_norm_stderr": 0.03511097439217402, "mc1": 0.29865361077111385, "mc1_stderr": 0.016021570613768542, "mc2": 0.455854048100616, "mc2_stderr": 0.01483939681310818 }, "harness|arc:challenge|25": { "acc": 0.5102389078498294, "acc_stderr": 0.014608326906285012, "acc_norm": 0.5435153583617748, "acc_norm_stderr": 0.01455594976049644 }, "harness|hellaswag|10": { "acc": 0.5936068512248556, "acc_stderr": 0.004901558132335522, "acc_norm": 0.7909778928500298, "acc_norm_stderr": 0.00405779217189357 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464242, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464242 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.48026315789473684, "acc_stderr": 0.040657710025626036, "acc_norm": 0.48026315789473684, "acc_norm_stderr": 0.040657710025626036 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5433962264150943, "acc_stderr": 0.03065674869673943, "acc_norm": 0.5433962264150943, "acc_norm_stderr": 0.03065674869673943 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5138888888888888, "acc_stderr": 0.041795966175810016, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.041795966175810016 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.41040462427745666, "acc_stderr": 0.03750757044895537, "acc_norm": 0.41040462427745666, "acc_norm_stderr": 0.03750757044895537 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.20588235294117646, "acc_stderr": 0.04023382273617747, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.04023382273617747 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4595744680851064, "acc_stderr": 0.03257901482099835, "acc_norm": 0.4595744680851064, "acc_norm_stderr": 0.03257901482099835 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.30701754385964913, "acc_stderr": 0.04339138322579862, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.04339138322579862 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.30158730158730157, "acc_stderr": 0.023636975996101806, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.023636975996101806 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "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": 0.535483870967742, "acc_stderr": 0.028372287797962935, "acc_norm": 0.535483870967742, "acc_norm_stderr": 0.028372287797962935 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3793103448275862, "acc_stderr": 0.03413963805906235, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.03413963805906235 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6484848484848484, "acc_stderr": 0.037282069986826503, "acc_norm": 0.6484848484848484, "acc_norm_stderr": 0.037282069986826503 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6212121212121212, "acc_stderr": 0.03456088731993747, "acc_norm": 0.6212121212121212, "acc_norm_stderr": 0.03456088731993747 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7305699481865285, "acc_stderr": 0.03201867122877794, "acc_norm": 0.7305699481865285, "acc_norm_stderr": 0.03201867122877794 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.47435897435897434, "acc_stderr": 0.025317649726448652, "acc_norm": 0.47435897435897434, "acc_norm_stderr": 0.025317649726448652 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26666666666666666, "acc_stderr": 0.026962424325073845, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.026962424325073845 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.0322529423239964, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.0322529423239964 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6844036697247706, "acc_stderr": 0.01992611751386967, "acc_norm": 0.6844036697247706, "acc_norm_stderr": 0.01992611751386967 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.03214952147802749, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.03214952147802749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6862745098039216, "acc_stderr": 0.03256685484460388, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.03256685484460388 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7341772151898734, "acc_stderr": 0.028756799629658342, "acc_norm": 0.7341772151898734, "acc_norm_stderr": 0.028756799629658342 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6188340807174888, "acc_stderr": 0.03259625118416828, "acc_norm": 0.6188340807174888, "acc_norm_stderr": 0.03259625118416828 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5954198473282443, "acc_stderr": 0.043046937953806645, "acc_norm": 0.5954198473282443, "acc_norm_stderr": 0.043046937953806645 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6033057851239669, "acc_stderr": 0.044658697805310094, "acc_norm": 0.6033057851239669, "acc_norm_stderr": 0.044658697805310094 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5648148148148148, "acc_stderr": 0.04792898170907061, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.04792898170907061 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5460122699386503, "acc_stderr": 0.0391170190467718, "acc_norm": 0.5460122699386503, "acc_norm_stderr": 0.0391170190467718 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4017857142857143, "acc_stderr": 0.04653333146973646, "acc_norm": 0.4017857142857143, "acc_norm_stderr": 0.04653333146973646 }, "harness|hendrycksTest-management|5": { "acc": 0.6699029126213593, "acc_stderr": 0.0465614711001235, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.0465614711001235 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7735042735042735, "acc_stderr": 0.027421007295392923, "acc_norm": 0.7735042735042735, "acc_norm_stderr": 0.027421007295392923 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6973180076628352, "acc_stderr": 0.016428781581749364, "acc_norm": 0.6973180076628352, "acc_norm_stderr": 0.016428781581749364 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5346820809248555, "acc_stderr": 0.026854257928258875, "acc_norm": 0.5346820809248555, "acc_norm_stderr": 0.026854257928258875 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2435754189944134, "acc_stderr": 0.014355911964767867, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.014355911964767867 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.545751633986928, "acc_stderr": 0.02850980780262659, "acc_norm": 0.545751633986928, "acc_norm_stderr": 0.02850980780262659 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6109324758842444, "acc_stderr": 0.027690337536485376, "acc_norm": 0.6109324758842444, "acc_norm_stderr": 0.027690337536485376 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.558641975308642, "acc_stderr": 0.027628737155668777, "acc_norm": 0.558641975308642, "acc_norm_stderr": 0.027628737155668777 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3900709219858156, "acc_stderr": 0.02909767559946393, "acc_norm": 0.3900709219858156, "acc_norm_stderr": 0.02909767559946393 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3709256844850065, "acc_stderr": 0.012337391684530312, "acc_norm": 0.3709256844850065, "acc_norm_stderr": 0.012337391684530312 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5110294117647058, "acc_stderr": 0.030365446477275675, "acc_norm": 0.5110294117647058, "acc_norm_stderr": 0.030365446477275675 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5, "acc_stderr": 0.020227834851568375, "acc_norm": 0.5, "acc_norm_stderr": 0.020227834851568375 }, "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": 0.5673469387755102, "acc_stderr": 0.031717528240626645, "acc_norm": 0.5673469387755102, "acc_norm_stderr": 0.031717528240626645 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6417910447761194, "acc_stderr": 0.03390393042268814, "acc_norm": 0.6417910447761194, "acc_norm_stderr": 0.03390393042268814 }, "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": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6900584795321637, "acc_stderr": 0.035469769593931624, "acc_norm": 0.6900584795321637, "acc_norm_stderr": 0.035469769593931624 }, "harness|truthfulqa:mc|0": { "mc1": 0.29865361077111385, "mc1_stderr": 0.016021570613768542, "mc2": 0.455854048100616, "mc2_stderr": 0.01483939681310818 }, "harness|winogrande|5": { "acc": 0.744277821625888, "acc_stderr": 0.012261253845440473 }, "harness|gsm8k|5": { "acc": 0.18726307808946172, "acc_stderr": 0.010745914199510815 } } ``` ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

tfnn/MESHY.AI_363_PLY_Creatures_Labelled

--- license: cc0-1.0 language: - en tags: - glb - 3d - asset - model - mesh - gltf - meshy - meshy.ai - creatures - humans - people - animals - monsters - robots - mythical - dragons pretty_name: MESHY.AI 363 PLY Creatures Labelled size_categories: - n<1K --- - [meshy_creatures.zip](https://huggingface.co/datasets/tfnn/MESHY.AI_363_PLY_Creatures_Labelled/resolve/main/meshy_creatures.zip?download=true) - 363 Samples [PLY](https://paulbourke.net/dataformats/ply/) ([Vertex Colored](https://github.com/VertexColor), Labelled). Refined from [this dataset](https://huggingface.co/datasets/tfnn/MESHY.AI_800_GLB_3D-Assets_Categorised_and_Labelled) but with fixed spelling mistakes and slightly improved labelling this dataset of 363 creatures includes `people`, `animals`, `monsters`, and `robots`. Along with some mythical dragons, mushroom people, frogs, four legged animals with two legs, etc. These models have been scaled to a unit sphere which is a normalised cubic scale multiplied by 0.55 which means they fit perfectly within a unit sphere. This dataset makes for good test cases where you just want to prove a network topology works, hastily, on a reasonably decent and small dataset. All models in this dataset are rougly the same category with the biggest deviation being two legged creatures to four legged creatures or bipedal robots with wheels rather than legs. Each model can range between 16,000 and 45,000 vertices, fairly uniform spacing between each vertex. Models are vertex colored. Special thanks to [MESHY.AI](https://meshy.ai) for generating the models for this dataset using their online service.

mapama247/chess_games_caissabase

--- license: apache-2.0 ---

yzhuang/autotree_pmlb_100000_clean2_sgosdt_l256_dim10_d3_sd0

--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: input_y_clean sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 2364400000 num_examples: 100000 - name: validation num_bytes: 236440000 num_examples: 10000 download_size: 622819971 dataset_size: 2600840000 --- # Dataset Card for "autotree_pmlb_100000_clean2_sgosdt_l256_dim10_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Ziyuan111/Urban_Tree_Canopy_in_Durham2

--- license: apache-2.0 language: - en size_categories: - 10K<n<100K --- # Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) ## Project Overview The Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage. ## Background and Rationale Durham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come. ## Data Sources and Methodology ### Data Sources We will leverage the following files from the Durham Trees & Planting Sites Dataset, as found on the Durham Open Data portal: - `GS_TreeInventory.shp` - `Trees_&_Planting_Sites.csv` - `Trees_%26_Planting_Sites.geojson` # Dataset Card for Urban Tree Inventory ## Dataset Description This dataset provides comprehensive information about urban trees within a specified area, including their physical characteristics, environmental benefits, and the economic value they add in terms of ecosystem services. ### Spatial Data (GeoJSON) **Format:** GeoJSON **Content:** - **Type:** `FeatureCollection` - A collection of feature objects. - **Features:** Each feature object represents a tree and contains: - **Type:** `Feature` - **Geometry:** `Point` (includes longitude and latitude of the tree location). - **Properties:** Detailed information about the tree (some fields may overlap with the CSV structure below). ### Tabular Data (CSV) **Format:** CSV **Columns:** - **X, Y:** Coordinates of the tree location. - **OBJECTID:** Unique identifier for the tree. - **streetaddress:** Street address nearest to the tree. - **city:** City where the tree is located. - **zipcode:** Zip code for the location of the tree. - **facilityid:** Identifier for the facility associated with the tree, if any. - **present:** Indication of whether the tree is currently present. - **genus, species, commonname:** Botanical and common names of the tree. - **plantingdate:** Date when the tree was planted. - **diameterin:** Diameter of the tree trunk in inches. - **heightft:** Height of the tree in feet. - **condition:** Health condition of the tree. - **contractwork:** Indicates if the tree has had any contract work done. - **neighborhood:** Neighborhood where the tree is located. - **program:** The program under which the tree was planted. - **plantingw:** Width of the planting site. - **plantingcond:** Condition of the planting site. - **underpwerlins:** Whether the tree is under power lines. - **matureheight:** The mature height of the tree. - **GlobalID:** A global unique identifier for the tree. - **created_user:** The user who created the record. - **created_date:** The date the record was created. - **last_edited_user:** The user who last edited the record. - **last_edited_date:** The date the record was last edited. #### Environmental and Economic Data: - **isoprene, monoterpene, vocs:** Emissions and absorption data for various compounds. - **coremoved_ozperyr, o3removed_ozperyr, etc.:** Annual pollutant removal metrics. - **o2production_lbperyr:** Annual oxygen production. - **carbonstorage_lb, carbonstorage_dol:** Carbon storage metrics. - **grosscarseq_lbperyr, grosscarseq_dolperyr:** Gross carbon sequestration. - **avoidrunoff_ft2peryr, avoidrunoff_dol2peryr:** Metrics related to stormwater runoff avoidance. - **totannbenefits_dolperyr:** Total annual dollar benefits from the tree. - **leafarea_sqft, potevapotran_cuftperyr, etc.:** Metrics related to the water cycle. - **heating_mbtuperyr, cooling_kwhperyr, etc.:** Energy savings related to the tree's impact on building energy use. ### Example Record **GeoJSON Feature:** ```json { "type": "Feature", "geometry": { "type": "Point", "coordinates": [-78.90863, 36.00441] }, "properties": { "OBJECTID": 2840940, "commonname": "Willow Oak", // Additional properties... } } ``` The `GS_TreeInventory.shp` file encompasses a range of attributes for each record: - **OBJECTID:** Unique identifier for each record. - **streetaddr:** Street address where the tree or planting site is located. - **city:** The city name, which is Durham. - **zipcode:** Postal code for the location. - **facilityid:** Identifier possibly linked to a facility or area associated with the tree. - **present:** Type of feature present, such as a tree or a planting site. - **genus:** Genus of the tree. - **species:** Species of the tree. - **commonname:** Common name of the tree. - **plantingda:** Date or year range when the tree was planted or the planting site was established. - ... ### Objectives 1. Combine Shapefile and CSV data into a comprehensive geospatial dataset using Python. 2. Apply Python libraries to uncover relationships between tree canopy data and urban development. 3. Provide practical insights and strategies for the expansion of Durham's urban tree canopy. 4. Produce analyses and visualizations with the GeoJSON file. ### Methodology Our analytical process within Google Colab will encompass: - **Data Preparation and Integration:** Using tools like Geopandas, Pandas, and PyShp to organize and combine spatial and tabular data. - **Geospatial Analysis:** Applying Shapely and Rtree for spatial analysis, and using SciPy or Statsmodels for statistical correlations. - **Visualization and Optimization:** Generating maps and graphs with Matplotlib, Seaborn, or Plotly, and utilizing optimization algorithms to suggest optimal planting locations. ## Deliverables 1. A collection of Google Colab Python notebooks that outline our analytical processes. 2. Interactive maps and visualizations that connect tree canopy coverage with urban development metrics. 3. An exhaustive report that contains our findings and recommendations for enhancing the urban canopy. ## Limitations - **Computational Resources:** The limited computational offerings of Google Colab may pose a challenge to the size of the datasets or the complexity of models we can employ. - **Data Quality:** The accuracy and currency of the data ultimately affect the precision of our recommendations. - **Sociopolitical Considerations:** Implementation of our data-driven suggestions must be reviewed within the context of local policy and community input. ## Conclusion DUCAEI aims to create a more verdant and livable urban landscape in Durham through this Python-based analytical project. By laying a strong foundation for data-informed decision-making, we hope to cultivate a thriving, green, and sustainable urban environment.

ainzOulgun/fqavrR

--- license: openrail ---

ctoraman/deprem-tweet-dataset

--- license: cc task_categories: - text-classification - token-classification language: - tr tags: - disaster-relief - disaster - earthquake - tweets - deprem - tweet-classification - ner size_categories: - 1K<n<10K --- Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster The annotated dataset is given at dataset.tsv. We annotate 1,000 tweets in Turkish if tweets call for help (i.e. request rescue, supply, or donation), and their entity tags (person, city, address, status). Column Name Description label Human annotation if tweet calls for help (binary classification) entities Human annotation of entity tags (i.e. person, city, address, and status). The format is [START_INDEX]:[END_INDEX]%[TAG_TYPE]. tweet_id Tweet ID from Twitter API. Other details can be found at https://github.com/avaapm/deprem Citation If you make use of this dataset, please cite following paper. @misc{toraman2023earthquake, doi = {10.48550/ARXIV.2302.13403}, url = {https://arxiv.org/abs/2302.13403}, author = {Toraman, Cagri and Kucukkaya, Izzet Emre and Ozcelik, Oguzhan and Sahin, Umitcan}, keywords = {Social and Information Networks (cs.SI), Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster}, publisher = {arXiv}, year = {2023}, copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} }

mpasila/DarkViperAU-Essays

--- language: - en --- This is just the raw text extract of his essays. This will be cleaned up later.

allenai/multixscience_sparse_max

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization paperswithcode_id: multi-xscience pretty_name: Multi-XScience --- This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used: - __query__: The `related_work` field of each example - __corpus__: The union of all documents in the `train`, `validation` and `test` splits - __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings - __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==20` Retrieval results on the `train` set: | Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.5482 | 0.2243 | 0.0547 | 0.4063 | Retrieval results on the `validation` set: | Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.5476 | 0.2209 | 0.0553 | 0.4026 | Retrieval results on the `test` set: | Recall@100 | Rprec | Precision@k | Recall@k | | ----------- | ----------- | ----------- | ----------- | | 0.5480 | 0.2272 | 0.055 | 0.4039 |

liuyanchen1015/MULTI_VALUE_rte_plural_postposed

--- 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: 890889 num_examples: 2389 - name: train num_bytes: 776257 num_examples: 1989 download_size: 1045708 dataset_size: 1667146 --- # Dataset Card for "MULTI_VALUE_rte_plural_postposed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/charlotte_fireemblem

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of charlotte (Fire Emblem) This is the dataset of charlotte (Fire Emblem), containing 285 images and their tags. The core tags of this character are `blonde_hair, long_hair, breasts, bow, blue_eyes, hair_bow, large_breasts, bangs, white_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 | 285 | 304.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 285 | 183.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 665 | 377.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 285 | 275.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 665 | 516.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/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/charlotte_fireemblem', 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 | 26 | ![](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, hetero, solo_focus, 1boy, penis, pussy, nipples, blush, uncensored, vaginal, nude, open_mouth, navel, sex_from_behind, spread_legs, testicles | | 1 | 18 | ![](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, 1girl, hetero, solo_focus, penis, nipples, paizuri, blush, nude, cum_on_breasts, smile, facial, uncensored | | 2 | 20 | ![](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, cleavage, solo, smile, navel, midriff, looking_at_viewer, shoulder_armor, bikini_armor, white_background, simple_background, spikes | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, solo, huge_breasts, looking_at_viewer, smile, thick_thighs, blush, blunt_bangs, short_shorts, ass, cleavage, simple_background, thighhighs, blue_background, denim_shorts, from_behind, looking_back | | 4 | 10 | ![](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, huge_penis, solo, uncensored, blush, erection, nipples, nude, open_mouth, testicles, navel, heart, animal_penis, blunt_bangs, ejaculation, futanari_masturbation, rolling_eyes, spread_legs, thighhighs, tongue, veiny_penis | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hetero | solo_focus | 1boy | penis | pussy | nipples | blush | uncensored | vaginal | nude | open_mouth | navel | sex_from_behind | spread_legs | testicles | paizuri | cum_on_breasts | smile | facial | cleavage | solo | midriff | looking_at_viewer | shoulder_armor | bikini_armor | white_background | simple_background | spikes | huge_breasts | thick_thighs | blunt_bangs | short_shorts | ass | thighhighs | blue_background | denim_shorts | from_behind | looking_back | huge_penis | erection | heart | animal_penis | ejaculation | futanari_masturbation | rolling_eyes | tongue | veiny_penis | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------------|:-------|:--------|:--------|:----------|:--------|:-------------|:----------|:-------|:-------------|:--------|:------------------|:--------------|:------------|:----------|:-----------------|:--------|:---------|:-----------|:-------|:----------|:--------------------|:-----------------|:---------------|:-------------------|:--------------------|:---------|:---------------|:---------------|:--------------|:---------------|:------|:-------------|:------------------|:---------------|:--------------|:---------------|:-------------|:-----------|:--------|:---------------|:--------------|:------------------------|:---------------|:---------|:--------------| | 0 | 26 | ![](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 | 18 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 20 | ![](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 | | | | | | | | | | | | | | | | | | | | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | | | | | X | | | | | | | | | | | X | | X | X | | X | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 4 | 10 | ![](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 |

CyberHarem/silk_fireemblem

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of silk (Fire Emblem) This is the dataset of silk (Fire Emblem), containing 107 images and their tags. The core tags of this character are `blue_hair, short_hair, breasts, blue_eyes, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 107 | 92.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 107 | 62.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 236 | 118.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 107 | 85.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 236 | 149.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/silk_fireemblem/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/silk_fireemblem', 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 | 64 | ![](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, dress, simple_background, smile, looking_at_viewer, veil, long_sleeves, white_background | | 1 | 11 | ![](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) | hetero, solo_focus, 1girl, blush, vaginal, large_breasts, nipples, open_mouth, mosaic_censoring, multiple_boys, completely_nude, cum_in_pussy, multiple_penises, spread_legs, 1boy, sweat, double_handjob, gangbang, green_eyes, pregnant, tongue | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | dress | simple_background | smile | looking_at_viewer | veil | long_sleeves | white_background | hetero | solo_focus | blush | vaginal | large_breasts | nipples | open_mouth | mosaic_censoring | multiple_boys | completely_nude | cum_in_pussy | multiple_penises | spread_legs | 1boy | sweat | double_handjob | gangbang | green_eyes | pregnant | tongue | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:--------|:--------------------|:-------|:---------------|:-------------------|:---------|:-------------|:--------|:----------|:----------------|:----------|:-------------|:-------------------|:----------------|:------------------|:---------------|:-------------------|:--------------|:-------|:--------|:-----------------|:-----------|:-------------|:-----------|:---------| | 0 | 64 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 1 | 11 | ![](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 |

FelixdoingAI/IP2P-adwm-128-5000

--- dataset_info: features: - name: original_prompt dtype: string - name: original_image dtype: image - name: edit_prompt dtype: string - name: edited_prompt dtype: string - name: edited_image dtype: image - name: adversarial_image dtype: image splits: - name: train num_bytes: 574509255.0 num_examples: 5000 download_size: 574446939 dataset_size: 574509255.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/namba_eri_idolmastercinderellagirls

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of namba_eri (THE iDOLM@STER: Cinderella Girls) This is the dataset of namba_eri (THE iDOLM@STER: Cinderella Girls), containing 32 images and their tags. The core tags of this character are `brown_hair, short_hair, hair_ornament, hairclip, green_eyes, wavy_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 | 32 | 20.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 32 | 18.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 55 | 30.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 32 | 20.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 55 | 32.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/namba_eri_idolmastercinderellagirls/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/namba_eri_idolmastercinderellagirls', 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 | 13 | ![](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, card_(medium), character_name, sun_symbol, skirt, open_mouth, orange_background, :d, looking_at_viewer, bow, breasts, grin | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | card_(medium) | character_name | sun_symbol | skirt | open_mouth | orange_background | :d | looking_at_viewer | bow | breasts | grin | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------------|:-----------------|:-------------|:--------|:-------------|:--------------------|:-----|:--------------------|:------|:----------|:-------| | 0 | 13 | ![](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 |

autoevaluate/autoeval-eval-xsum-default-8e4fa8-60494145409

--- type: predictions tags: - autotrain - evaluation datasets: - xsum eval_info: task: summarization model: morenolq/bart-base-xsum metrics: [] dataset_name: xsum dataset_config: default dataset_split: test col_mapping: text: document target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: morenolq/bart-base-xsum * Dataset: xsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@zuzannad1](https://huggingface.co/zuzannad1) for evaluating this model.

yzhuang/autotree_automl_heloc_gosdt_l512_d3_sd3

--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: int64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: int64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 11682400000 num_examples: 100000 - name: validation num_bytes: 1168240000 num_examples: 10000 download_size: 1508311822 dataset_size: 12850640000 --- # Dataset Card for "autotree_automl_heloc_gosdt_l512_d3_sd3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

davidreyblanco/distilabel-math-instructions

--- dataset_info: features: - name: instructions dtype: string splits: - name: train num_bytes: 169849 num_examples: 1840 download_size: 56464 dataset_size: 169849 configs: - config_name: default data_files: - split: train path: data/train-* ---

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-110000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 969978 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

mHossain/final_train_v2_90000

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: input_text dtype: string - name: target_text dtype: string - name: prefix dtype: string splits: - name: train num_bytes: 9136964.7 num_examples: 27000 - name: test num_bytes: 1015218.3 num_examples: 3000 download_size: 4451000 dataset_size: 10152183.0 --- # Dataset Card for "final_train_v2_90000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

naorm/desktop-blip-large

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 12745883.0 num_examples: 51 download_size: 12428402 dataset_size: 12745883.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

yiyic/eval_clir_mtg

--- dataset_info: features: - name: text dtype: string splits: - name: mtg_en num_bytes: 48454 num_examples: 500 - name: mtg_de num_bytes: 57186 num_examples: 500 - name: mtg_es num_bytes: 51263 num_examples: 500 - name: mtg_fr num_bytes: 59140 num_examples: 500 - name: nq_en num_bytes: 306325 num_examples: 500 - name: en_rt num_bytes: 49483 num_examples: 500 - name: de_en_multi8_test1 num_bytes: 1308045 num_examples: 1000 - name: de_fr_multi8_test1 num_bytes: 1310347 num_examples: 1000 - name: de_es_multi8_test1 num_bytes: 1310100 num_examples: 1000 - name: en_de_multi8_test1 num_bytes: 1120561 num_examples: 1000 - name: en_fr_multi8_test1 num_bytes: 1122148 num_examples: 1000 - name: en_es_multi8_test1 num_bytes: 1121901 num_examples: 1000 - name: es_en_multi8_test1 num_bytes: 1093157 num_examples: 1000 - name: es_fr_multi8_test1 num_bytes: 1095459 num_examples: 1000 - name: es_de_multi8_test1 num_bytes: 1093872 num_examples: 1000 - name: fr_en_multi8_test1 num_bytes: 1134440 num_examples: 1000 - name: fr_de_multi8_test1 num_bytes: 1135155 num_examples: 1000 - name: fr_es_multi8_test1 num_bytes: 1136495 num_examples: 1000 download_size: 9037857 dataset_size: 14553531 configs: - config_name: default data_files: - split: mtg_en path: data/mtg_en-* - split: mtg_de path: data/mtg_de-* - split: mtg_es path: data/mtg_es-* - split: mtg_fr path: data/mtg_fr-* - split: nq_en path: data/nq_en-* - split: en_rt path: data/en_rt-* - 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-* ---

YashaP/tinyscitldr

--- dataset_info: features: - name: instruction dtype: string - name: output sequence: string splits: - name: train num_bytes: 1368087 num_examples: 1000 download_size: 737186 dataset_size: 1368087 configs: - config_name: default data_files: - split: train path: data/train-* ---

foilfoilfoil/LaminiChatML

--- license: other ---

Rasu23/iapp_chosen_reject_orpov1

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: question dtype: string - name: context dtype: string - name: index_column dtype: int64 - name: text_prompt dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 33360071 num_examples: 5610 download_size: 5948625 dataset_size: 33360071 configs: - config_name: default data_files: - split: train path: data/train-* ---

SEACrowd/posp

--- tags: - pos-tagging language: - ind --- # posp POSP is a POS Tagging dataset containing 8400 sentences, collected from Indonesian news website with 26 POS tag classes. The POS tag labels follow the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention. POSP dataset is splitted into 3 sets with 6720 train, 840 validation, and 840 test data. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @inproceedings{hoesen2018investigating, title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger}, author={Devin Hoesen and Ayu Purwarianti}, booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)}, pages={35--38}, year={2018}, organization={IEEE} } @inproceedings{wilie2020indonlu, title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding}, author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti}, booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing}, year={2020} } ``` ## License Creative Common Attribution Share-Alike 4.0 International ## Homepage [https://github.com/IndoNLP/indonlu](https://github.com/IndoNLP/indonlu) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)

tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3

--- dataset_info: features: - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train_qa num_bytes: 697367 num_examples: 6000 - name: train_ic_qa num_bytes: 4540536 num_examples: 6000 - name: train_recite_qa num_bytes: 4546536 num_examples: 6000 - name: eval_qa num_bytes: 752802 num_examples: 6489 - name: eval_ic_qa num_bytes: 4906186 num_examples: 6489 - name: eval_recite_qa num_bytes: 4912675 num_examples: 6489 - name: all_docs num_bytes: 7126313 num_examples: 10925 - name: all_docs_eval num_bytes: 7125701 num_examples: 10925 - name: train num_bytes: 3818906 num_examples: 6000 - name: validation num_bytes: 4103798 num_examples: 6489 download_size: 26446330 dataset_size: 42530820 configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_ic_qa path: data/train_ic_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_ic_qa path: data/eval_ic_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: all_docs path: data/all_docs-* - split: all_docs_eval path: data/all_docs_eval-* - split: train path: data/train-* - split: validation path: data/validation-* ---

CyberHarem/rosmontis_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of rosmontis/ロスモンティス/迷迭香 (Arknights) This is the dataset of rosmontis/ロスモンティス/迷迭香 (Arknights), containing 500 images and their tags. The core tags of this character are `animal_ears, cat_ears, long_hair, cat_girl, animal_ear_fluff, green_eyes, grey_hair, white_hair, tail, cat_tail`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 872.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 425.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_arknights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1280 | 955.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_arknights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 732.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1280 | 1.44 GiB | [Download](https://huggingface.co/datasets/CyberHarem/rosmontis_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/rosmontis_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 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, black_gloves, fingerless_gloves, hood_down, jacket, solo, white_dress, holding_book, infection_monitor_(arknights), looking_at_viewer, closed_mouth, earpiece, hooded_cloak | | 1 | 6 | ![](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_footwear, black_gloves, closed_mouth, fingerless_gloves, full_body, solo, white_dress, boots, looking_at_viewer, earpiece, hood_down, hooded_cloak, infection_monitor_(arknights), thigh_strap | | 2 | 10 | ![](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, hood_down, looking_at_viewer, solo, upper_body, jacket, blush, simple_background, white_background, closed_mouth | | 3 | 13 | ![](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, collar, looking_at_viewer, sleeveless_dress, solo, white_dress, bare_shoulders, official_alternate_costume, barefoot, twintails, closed_mouth, full_body, very_long_hair, bare_legs, feet, mechanical_wings, toes, white_background, colored_eyelashes, simple_background, sitting | | 4 | 10 | ![](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) | 1boy, 1girl, blush, hetero, sex, nipples, sweat, vaginal, penis, solo_focus, open_mouth, small_breasts, black_gloves, fingerless_gloves, spread_legs, thigh_strap, dress, mosaic_censoring, navel, straddling, closed_eyes, coat, cum_in_pussy, girl_on_top, looking_at_viewer, missionary, on_back, pov | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | fingerless_gloves | hood_down | jacket | solo | white_dress | holding_book | infection_monitor_(arknights) | looking_at_viewer | closed_mouth | earpiece | hooded_cloak | black_footwear | full_body | boots | thigh_strap | upper_body | blush | simple_background | white_background | collar | sleeveless_dress | bare_shoulders | official_alternate_costume | barefoot | twintails | very_long_hair | bare_legs | feet | mechanical_wings | toes | colored_eyelashes | sitting | 1boy | hetero | sex | nipples | sweat | vaginal | penis | solo_focus | open_mouth | small_breasts | spread_legs | dress | mosaic_censoring | navel | straddling | closed_eyes | coat | cum_in_pussy | girl_on_top | missionary | on_back | pov | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:------------|:---------|:-------|:--------------|:---------------|:--------------------------------|:--------------------|:---------------|:-----------|:---------------|:-----------------|:------------|:--------|:--------------|:-------------|:--------|:--------------------|:-------------------|:---------|:-------------------|:-----------------|:-----------------------------|:-----------|:------------|:-----------------|:------------|:-------|:-------------------|:-------|:--------------------|:----------|:-------|:---------|:------|:----------|:--------|:----------|:--------|:-------------|:-------------|:----------------|:--------------|:--------|:-------------------|:--------|:-------------|:--------------|:-------|:---------------|:--------------|:-------------|:----------|:------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 10 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 13 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | 4 | 10 | ![](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 | X |

satyambarnwal/cartoonizer-dataset

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 31656230.0 num_examples: 50 download_size: 31658428 dataset_size: 31656230.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

asun17904/wiki2017_bank_examples

--- dataset_info: features: - name: section_texts sequence: string - name: section_titles sequence: string - name: title dtype: string - name: soi sequence: string - name: other sequence: string splits: - name: train num_bytes: 3467283652 num_examples: 163992 download_size: 0 dataset_size: 3467283652 --- # Dataset Card for "wiki2017_bank_examples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

atmallen/companies_azaria_mitchell

--- dataset_info: features: - name: statement dtype: string - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 80207.2 num_examples: 960 - name: test num_bytes: 20051.8 num_examples: 240 download_size: 34846 dataset_size: 100259.0 --- # Dataset Card for "companies_azaria_mitchell" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)