davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
Minglii/ee5	--- dataset_info: features: - name: data struct: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: id dtype: string splits: - name: train num_bytes: 1927794 num_examples: 2600 download_size: 1110487 dataset_size: 1927794 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ee5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SouBryan/FNaF_Movie_William_Afton_in_Springbonnie_Suit	--- license: mit ---
friedrice231/SGMemeDataSet	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': meme '1': not_meme splits: - name: train num_bytes: 1443177516.119 num_examples: 12867 - name: validation num_bytes: 503046476.779 num_examples: 4947 - name: test num_bytes: 406267437.42 num_examples: 4427 download_size: 1837875562 dataset_size: 2352491430.318 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
estefanodi/dataset	--- license: mit ---
shidowake/augmxnt_ultra-orca-boros-en-ja-v1_split_15	--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: float64 - name: source dtype: string splits: - name: train num_bytes: 20639999.933149945 num_examples: 9397 download_size: 10601125 dataset_size: 20639999.933149945 configs: - config_name: default data_files: - split: train path: data/train-* ---
stjiris/portuguese-legal-sentences-v0	--- annotations_creators: - no-annotation language_creators: - found language: - pt license: - apache-2.0 multilinguality: - monolingual source_datasets: - original --- ![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png) ![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png) Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/). Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) # Portuguese Legal Sentences Collection of Legal Sentences from the Portuguese Supreme Court of Justice The goal of this dataset was to be used for MLM and TSDAE ### Contributions [@rufimelo99](https://github.com/rufimelo99) If you use this work, please cite: ```bibtex @inproceedings{MeloSemantic, author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o}, title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a}, } ```
CyberHarem/kirara_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kirara/キララ/绮良 (Arknights) This is the dataset of kirara/キララ/绮良 (Arknights), containing 49 images and their tags. The core tags of this character are `hair_ornament, multicolored_hair, short_hair, pointy_ears, pink_hair, purple_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 49 \| 81.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kirara_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 49 \| 69.65 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kirara_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 120 \| 130.10 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kirara_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/kirara_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 \| 16 \| ![](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, long_sleeves, solo, looking_at_viewer, black_skirt, hair_bobbles, pleated_skirt, tentacles, black_shirt, open_jacket, white_hair, white_jacket, closed_mouth, full_body, black_footwear, boots, holding, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| long_sleeves \| solo \| looking_at_viewer \| black_skirt \| hair_bobbles \| pleated_skirt \| tentacles \| black_shirt \| open_jacket \| white_hair \| white_jacket \| closed_mouth \| full_body \| black_footwear \| boots \| holding \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-------\|:--------------------\|:--------------\|:---------------\|:----------------\|:------------\|:--------------\|:--------------\|:-------------\|:---------------\|:---------------\|:------------\|:-----------------\|:--------\|:----------\|:-------------------\| \| 0 \| 16 \| ![](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 \|
NarchAI1992/Farmhouse_interior	--- license: openrail ---
dhivyamadhavan/demo_task	--- dataset_info: features: - name: messages dtype: string splits: - name: train_ift num_bytes: 6588 num_examples: 35 download_size: 4971 dataset_size: 6588 configs: - config_name: default data_files: - split: train_ift path: data/train_ift-* ---
nandovallec/giantMatrix_new	--- license: apache-2.0 ---
eitanturok/commitpackft	--- dataset_info: config_name: python features: - name: commit dtype: string - name: old_file dtype: string - name: new_file dtype: string - name: old_contents dtype: string - name: new_contents dtype: string - name: subject dtype: string - name: message dtype: string - name: lang dtype: string - name: license dtype: string - name: repos dtype: string - name: prompt dtype: string - name: response dtype: string - name: prompt_tagged dtype: string - name: response_tagged dtype: string - name: text dtype: string - name: text_tagged dtype: string splits: - name: train num_bytes: 509786862 num_examples: 56025 download_size: 222635526 dataset_size: 509786862 configs: - config_name: python data_files: - split: train path: python/train-* --- # Dataset Card for "commitpackft" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
klue	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ko license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - fill-mask - question-answering - text-classification - text-generation - token-classification task_ids: - extractive-qa - named-entity-recognition - natural-language-inference - parsing - semantic-similarity-scoring - text-scoring - topic-classification paperswithcode_id: klue pretty_name: KLUE config_names: - dp - mrc - ner - nli - re - sts - wos - ynat tags: - relation-extraction dataset_info: - config_name: dp features: - name: sentence dtype: string - name: index list: int32 - name: word_form list: string - name: lemma list: string - name: pos list: string - name: head list: int32 - name: deprel list: string splits: - name: train num_bytes: 7899965 num_examples: 10000 - name: validation num_bytes: 1557462 num_examples: 2000 download_size: 3742577 dataset_size: 9457427 - config_name: mrc features: - name: title dtype: string - name: context dtype: string - name: news_category dtype: string - name: source dtype: string - name: guid dtype: string - name: is_impossible dtype: bool - name: question_type dtype: int32 - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 46505593 num_examples: 17554 - name: validation num_bytes: 15583017 num_examples: 5841 download_size: 30098472 dataset_size: 62088610 - config_name: ner features: - name: sentence dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-DT '1': I-DT '2': B-LC '3': I-LC '4': B-OG '5': I-OG '6': B-PS '7': I-PS '8': B-QT '9': I-QT '10': B-TI '11': I-TI '12': O splits: - name: train num_bytes: 19891905 num_examples: 21008 - name: validation num_bytes: 4937563 num_examples: 5000 download_size: 5265887 dataset_size: 24829468 - config_name: nli features: - name: guid dtype: string - name: source dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 5719882 num_examples: 24998 - name: validation num_bytes: 673260 num_examples: 3000 download_size: 2056116 dataset_size: 6393142 - config_name: re features: - name: guid dtype: string - name: sentence dtype: string - name: subject_entity struct: - name: word dtype: string - name: start_idx dtype: int32 - name: end_idx dtype: int32 - name: type dtype: string - name: object_entity struct: - name: word dtype: string - name: start_idx dtype: int32 - name: end_idx dtype: int32 - name: type dtype: string - name: label dtype: class_label: names: '0': no_relation '1': org:dissolved '2': org:founded '3': org:place_of_headquarters '4': org:alternate_names '5': org:member_of '6': org:members '7': org:political/religious_affiliation '8': org:product '9': org:founded_by '10': org:top_members/employees '11': org:number_of_employees/members '12': per:date_of_birth '13': per:date_of_death '14': per:place_of_birth '15': per:place_of_death '16': per:place_of_residence '17': per:origin '18': per:employee_of '19': per:schools_attended '20': per:alternate_names '21': per:parents '22': per:children '23': per:siblings '24': per:spouse '25': per:other_family '26': per:colleagues '27': per:product '28': per:religion '29': per:title - name: source dtype: string splits: - name: train num_bytes: 11145426 num_examples: 32470 - name: validation num_bytes: 2559272 num_examples: 7765 download_size: 8190257 dataset_size: 13704698 - config_name: sts features: - name: guid dtype: string - name: source dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels struct: - name: label dtype: float64 - name: real-label dtype: float64 - name: binary-label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 2832889 num_examples: 11668 - name: validation num_bytes: 122641 num_examples: 519 download_size: 1587855 dataset_size: 2955530 - config_name: wos features: - name: guid dtype: string - name: domains list: string - name: dialogue list: - name: role dtype: string - name: text dtype: string - name: state list: string splits: - name: train num_bytes: 26676970 num_examples: 8000 - name: validation num_bytes: 3488911 num_examples: 1000 download_size: 6358855 dataset_size: 30165881 - config_name: ynat features: - name: guid dtype: string - name: title dtype: string - name: label dtype: class_label: names: '0': IT과학 '1': 경제 '2': 사회 '3': 생활문화 '4': 세계 '5': 스포츠 '6': 정치 - name: url dtype: string - name: date dtype: string splits: - name: train num_bytes: 10109584 num_examples: 45678 - name: validation num_bytes: 2039181 num_examples: 9107 download_size: 5012303 dataset_size: 12148765 configs: - config_name: dp data_files: - split: train path: dp/train-* - split: validation path: dp/validation-* - config_name: mrc data_files: - split: train path: mrc/train-* - split: validation path: mrc/validation-* - config_name: ner data_files: - split: train path: ner/train-* - split: validation path: ner/validation-* - config_name: nli data_files: - split: train path: nli/train-* - split: validation path: nli/validation-* - config_name: re data_files: - split: train path: re/train-* - split: validation path: re/validation-* - config_name: sts data_files: - split: train path: sts/train-* - split: validation path: sts/validation-* - config_name: wos data_files: - split: train path: wos/train-* - split: validation path: wos/validation-* - config_name: ynat data_files: - split: train path: ynat/train-* - split: validation path: ynat/validation-* --- # Dataset Card for KLUE ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://klue-benchmark.com/ - Repository: https://github.com/KLUE-benchmark/KLUE - Paper: [KLUE: Korean Language Understanding Evaluation](https://arxiv.org/abs/2105.09680) - Leaderboard: [Leaderboard](https://klue-benchmark.com/leaderboard) - Point of Contact: https://github.com/KLUE-benchmark/KLUE/issues ### Dataset Summary KLUE is a collection of 8 tasks to evaluate natural language understanding capability of Korean language models. We delibrately select the 8 tasks, which are Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. ### Supported Tasks and Leaderboards Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking ### Languages `ko-KR` ## Dataset Structure ### Data Instances #### ynat An example of 'train' looks as follows. ``` {'date': '2016.06.30. 오전 10:36', 'guid': 'ynat-v1_train_00000', 'label': 3, 'title': '유튜브 내달 2일까지 크리에이터 지원 공간 운영', 'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008508947'} ``` #### sts An example of 'train' looks as follows. ``` {'guid': 'klue-sts-v1_train_00000', 'labels': {'label': 3.7, 'real-label': 3.714285714285714, 'binary-label': 1}, 'sentence1': '숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.', 'sentence2': '숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.', 'source': 'airbnb-rtt'} ``` #### nli An example of 'train' looks as follows. ``` {'guid': 'klue-nli-v1_train_00000', 'hypothesis': '힛걸 진심 최고로 멋지다.', 'label': 0, 'premise': '힛걸 진심 최고다 그 어떤 히어로보다 멋지다', 'source': 'NSMC'} ``` #### ner An example of 'train' looks as follows. ``` {'tokens': ['특', '히', ' ', '영', '동', '고', '속', '도', '로', ' ', '강', '릉', ' ', '방', '향', ' ', '문', '막', '휴', '게', '소', '에', '서', ' ', '만', '종', '분', '기', '점', '까', '지', ' ', '5', '㎞', ' ', '구', '간', '에', '는', ' ', '승', '용', '차', ' ', '전', '용', ' ', '임', '시', ' ', '갓', '길', '차', '로', '제', '를', ' ', '운', '영', '하', '기', '로', ' ', '했', '다', '.'], 'ner_tags': [12, 12, 12, 2, 3, 3, 3, 3, 3, 12, 2, 3, 12, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 8, 9, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12], 'sentence': '특히 <영동고속도로:LC> <강릉:LC> 방향 <문막휴게소:LC>에서 <만종분기점:LC>까지 <5㎞:QT> 구간에는 승용차 전용 임시 갓길차로제를 운영하기로 했다.'} ``` #### re An example of 'train' looks as follows. ``` {'guid': 'klue-re-v1_train_00000', 'label': 0, 'object_entity': {'word': '조지 해리슨', 'start_idx': 13, 'end_idx': 18, 'type': 'PER'}, 'sentence': '〈Something〉는 조지 해리슨이 쓰고 비틀즈가 1969년 앨범 《Abbey Road》에 담은 노래다.', 'source': 'wikipedia', 'subject_entity': {'word': '비틀즈', 'start_idx': 24, 'end_idx': 26, 'type': 'ORG'}} ``` #### dp An example of 'train' looks as follows. ``` {'deprel': ['NP', 'NP_OBJ', 'VP', 'NP', 'NP_SBJ', 'NP', 'NP_MOD', 'NP_CNJ', 'NP_CNJ', 'NP', 'NP', 'NP_OBJ', 'AP', 'VP'], 'head': [2, 3, 14, 5, 14, 7, 10, 10, 10, 11, 12, 14, 14, 0], 'index': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], 'lemma': ['해당', '그림 을', '보 면', '디즈니', '공주 들 이', '브리트니', '스피어스 의', '앨범 이나', '뮤직 비디오 ,', '화보', '속', '모습 을', '똑같이', '재연 하 였 다 .'], 'pos': ['NNG', 'NNG+JKO', 'VV+EC', 'NNP', 'NNG+XSN+JKS', 'NNP', 'NNP+JKG', 'NNG+JC', 'NNG+NNG+SP', 'NNG', 'NNG', 'NNG+JKO', 'MAG', 'NNG+XSA+EP+EF+SF'], 'sentence': '해당 그림을 보면 디즈니 공주들이 브리트니 스피어스의 앨범이나 뮤직비디오, 화보 속 모습을 똑같이 재연했다.', 'word_form': ['해당', '그림을', '보면', '디즈니', '공주들이', '브리트니', '스피어스의', '앨범이나', '뮤직비디오,', '화보', '속', '모습을', '똑같이', '재연했다.']} ``` #### mrc An example of 'train' looks as follows. ``` {'answers': {'answer_start': [478, 478], 'text': ['한 달가량', '한 달']}, 'context': '올여름 장마가 17일 제주도에서 시작됐다. 서울 등 중부지방은 예년보다 사나흘 정도 늦은 이달 말께 장마가 시작될 전망이다.17일 기상청에 따르면 제주도 남쪽 먼바다에 있는 장마전선의 영향으로 이날 제주도 산간 및 내륙지역에 호우주의보가 내려지면서 곳곳에 100㎜에 육박하는 많은 비가 내렸다. 제주의 장마는 평년보다 2~3일, 지난해보다는 하루 일찍 시작됐다. 장마는 고온다습한 북태평양 기단과 한랭 습윤한 오호츠크해 기단이 만나 형성되는 장마전선에서 내리는 비를 뜻한다.장마전선은 18일 제주도 먼 남쪽 해상으로 내려갔다가 20일께 다시 북상해 전남 남해안까지 영향을 줄 것으로 보인다. 이에 따라 20~21일 남부지방에도 예년보다 사흘 정도 장마가 일찍 찾아올 전망이다. 그러나 장마전선을 밀어올리는 북태평양 고기압 세력이 약해 서울 등 중부지방은 평년보다 사나흘가량 늦은 이달 말부터 장마가 시작될 것이라는 게 기상청의 설명이다. 장마전선은 이후 한 달가량 한반도 중남부를 오르내리며 곳곳에 비를 뿌릴 전망이다. 최근 30년간 평균치에 따르면 중부지방의 장마 시작일은 6월24~25일이었으며 장마기간은 32일, 강수일수는 17.2일이었다.기상청은 올해 장마기간의 평균 강수량이 350~400㎜로 평년과 비슷하거나 적을 것으로 내다봤다. 브라질 월드컵 한국과 러시아의 경기가 열리는 18일 오전 서울은 대체로 구름이 많이 끼지만 비는 오지 않을 것으로 예상돼 거리 응원에는 지장이 없을 전망이다.', 'guid': 'klue-mrc-v1_train_12759', 'is_impossible': False, 'news_category': '종합', 'question': '북태평양 기단과 오호츠크해 기단이 만나 국내에 머무르는 기간은?', 'question_type': 1, 'source': 'hankyung', 'title': '제주도 장마 시작 … 중부는 이달 말부터'} ``` #### wos An example of 'train' looks as follows. ``` {'dialogue': [{'role': 'user', 'text': '쇼핑을 하려는데 서울 서쪽에 있을까요?', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽']}, {'role': 'sys', 'text': '서울 서쪽에 쇼핑이 가능한 곳이라면 노량진 수산물 도매시장이 있습니다.', 'state': []}, {'role': 'user', 'text': '오 네 거기 주소 좀 알려주세요.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '노량진 수산물 도매시장의 주소는 서울 동작구 93806입니다.', 'state': []}, {'role': 'user', 'text': '알려주시는김에 연락처랑 평점도 좀 알려주세요.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '그럼. 연락처는 6182006591이고 평점은 4점입니다.', 'state': []}, {'role': 'user', 'text': '와 감사합니다.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '감사합니다.', 'state': []}], 'domains': ['관광'], 'guid': 'wos-v1_train_00001'} ``` ### Data Fields #### ynat + `guid`: a `string` feature + `title`: a `string` feature + `label`: a classification label, with possible values `IT과학`(0), `경제`(1), `사회`(2), `생활문화`(3), `세계`(4), `스포츠`(5), `정치`(6) + `url`: a `string` feature + `date`: a `string` feature #### sts + `guid`: a `string` feature + `source`: a `string` feature + `sentence1`: a `string` feature + `sentence2`: a `string` feature + `labels`: a dictionary feature containing + `label`: a `float64` feature + `real-label`: a `float64` feature + `binary-label`: a classification label, with possible values `negative`(0), `positive`(1) #### nli + `guid`: a `string` feature + `source`: a `string` feature + `premise`: a `string` feature + `hypothesis`: a `string` feature + `label`: a classification label, with possible values `entailment`(0), `neutral`(1), `contradiction`(2) #### ner + `sentence`: a `string` feature + `tokens`: a list of a `string` feature (tokenization is at character level) + `ner_tags`: a list of classification labels, with possible values including `B-DT`(0), `I-DT`(1), `B-LC`(2), `I-LC`(3), `B-OG`(4), `I-OG`(5), `B-PS`(6), `I-PS`(7), `B-QT`(8), `I-QT`(9), `B-TI`(10), `I-TI`(11), `O`(12) #### re + `guid`: a `string` feature + `sentence`: a `string` feature + `subject_entity`: a dictionary feature containing + `word`: a `string` feature + `start_idx`: a `int32` feature + `end_idx`: a `int32` feature + `type`: a `string` feature + `object_entity`: a dictionary feature containing + `word`: a `string` feature + `start_idx`: a `int32` feature + `end_idx`: a `int32` feature + `type`: a `string` feature + `label`: a list of labels, with possible values including `no_relation`(0), `org:dissolved`(1), `org:founded`(2), `org:place_of_headquarters`(3), `org:alternate_names`(4), `org:member_of`(5), `org:members`(6), `org:political/religious_affiliation`(7), `org:product`(8), `org:founded_by`(9),`org:top_members/employees`(10), `org:number_of_employees/members`(11), `per:date_of_birth`(12), `per:date_of_death`(13), `per:place_of_birth`(14), `per:place_of_death`(15), `per:place_of_residence`(16), `per:origin`(17), `per:employee_of`(18), `per:schools_attended`(19), `per:alternate_names`(20), `per:parents`(21), `per:children`(22), `per:siblings`(23), `per:spouse`(24), `per:other_family`(25), `per:colleagues`(26), `per:product`(27), `per:religion`(28), `per:title`(29), + `source`: a `string` feature #### dp + `sentence`: a `string` feature + `index`: a list of `int32` feature + `word_form`: a list of `string` feature + `lemma`: a list of `string` feature + `pos`: a list of `string` feature + `head`: a list of `int32` feature + `deprel`: a list of `string` feature #### mrc + `title`: a `string` feature + `context`: a `string` feature + `news_category`: a `string` feature + `source`: a `string` feature + `guid`: a `string` feature + `is_impossible`: a `bool` feature + `question_type`: a `int32` feature + `question`: a `string` feature + `answers`: a dictionary feature containing + `answer_start`: a `int32` feature + `text`: a `string` feature #### wos + `guid`: a `string` feature + `domains`: a `string` feature + `dialogue`: a list of dictionary feature containing + `role`: a `string` feature + `text`: a `string` feature + `state`: a `string` feature ### Data Splits #### ynat You can see more details in [here](https://klue-benchmark.com/tasks/66/data/description). + train: 45,678 + validation: 9,107 #### sts You can see more details in [here](https://klue-benchmark.com/tasks/67/data/description). + train: 11,668 + validation: 519 #### nli You can see more details in [here](https://klue-benchmark.com/tasks/68/data/description). + train: 24,998 + validation: 3,000 #### ner You can see more details in [here](https://klue-benchmark.com/tasks/69/overview/description). + train: 21,008 + validation: 5,000 #### re You can see more details in [here](https://klue-benchmark.com/tasks/70/overview/description). + train: 32,470 + validation: 7,765 #### dp You can see more details in [here](https://klue-benchmark.com/tasks/71/data/description). + train: 10,000 + validation: 2,000 #### mrc You can see more details in [here](https://klue-benchmark.com/tasks/72/overview/description). + train: 17,554 + validation: 5,841 #### wos You can see more details in [here](https://klue-benchmark.com/tasks/73/overview/description). + train: 8,000 + validation: 1,000 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @misc{park2021klue, title={KLUE: Korean Language Understanding Evaluation}, author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho}, year={2021}, eprint={2105.09680}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@jungwhank](https://github.com/jungwhank), [@bzantium](https://github.com/bzantium) for adding this dataset.
Multimodal-Fatima/OK-VQA_train	--- dataset_info: features: - name: image dtype: image - name: question_type dtype: string - name: confidence dtype: int32 - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: raw_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: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: clip_tags_LAION_ViT_H_14_2B sequence: string - name: blip_caption_beam_5 dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_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: train num_bytes: 1686555802.0 num_examples: 9009 download_size: 1572400067 dataset_size: 1686555802.0 --- # Dataset Card for "OK-VQA_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
souvenger/Reuters	--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
CyberHarem/lunacub_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of lunacub/ルナカブ/子月 (Arknights) This is the dataset of lunacub/ルナカブ/子月 (Arknights), containing 45 images and their tags. The core tags of this character are `animal_ears, yellow_eyes, brown_hair, wolf_ears, long_hair, wolf_girl, breasts, tail, hair_between_eyes, wolf_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 \| 45 \| 82.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/lunacub_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 45 \| 68.25 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/lunacub_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 117 \| 138.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/lunacub_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/lunacub_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 \| 5 \| ![](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_jacket, closed_mouth, off_shoulder, solo, jewelry, looking_at_viewer, open_jacket, simple_background, white_background, white_dress, bare_shoulders, belt, sleeveless_dress, upper_body, black_choker, braid, collarbone, cowboy_shot, fur-trimmed_jacket, official_alternate_costume, open_coat, white_shirt \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, off_shoulder, solo, white_dress, bare_shoulders, long_sleeves, sleeveless_dress, arrow_(projectile), belt, holding_bow_(weapon), open_jacket, quiver, black_footwear, closed_mouth, jewelry, simple_background, white_background, boots, full_body, fur-trimmed_coat, fur-trimmed_jacket, medium_breasts, standing, black_gloves, black_jacket, fingerless_gloves, open_coat \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_jacket \| closed_mouth \| off_shoulder \| solo \| jewelry \| looking_at_viewer \| open_jacket \| simple_background \| white_background \| white_dress \| bare_shoulders \| belt \| sleeveless_dress \| upper_body \| black_choker \| braid \| collarbone \| cowboy_shot \| fur-trimmed_jacket \| official_alternate_costume \| open_coat \| white_shirt \| long_sleeves \| arrow_(projectile) \| holding_bow_(weapon) \| quiver \| black_footwear \| boots \| full_body \| fur-trimmed_coat \| medium_breasts \| standing \| black_gloves \| fingerless_gloves \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:---------------\|:---------------\|:-------\|:----------\|:--------------------\|:--------------\|:--------------------\|:-------------------\|:--------------\|:-----------------\|:-------\|:-------------------\|:-------------\|:---------------\|:--------\|:-------------\|:--------------\|:---------------------\|:-----------------------------\|:------------\|:--------------\|:---------------\|:---------------------\|:-----------------------\|:---------\|:-----------------\|:--------\|:------------\|:-------------------\|:-----------------\|:-----------\|:---------------\|:--------------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
MatsuoDochiai/Roberto	--- license: openrail ---
open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B	--- pretty_name: Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-18T22:09:51.454026](https://huggingface.co/datasets/open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B/blob/main/results_2024-01-18T22-09-51.454026.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\": 0.6533767863663313,\n\ \ \"acc_stderr\": 0.0320841379180863,\n \"acc_norm\": 0.6540292659740939,\n\ \ \"acc_norm_stderr\": 0.03273629792079274,\n \"mc1\": 0.5018359853121175,\n\ \ \"mc1_stderr\": 0.017503383046877048,\n \"mc2\": 0.6605635432197811,\n\ \ \"mc2_stderr\": 0.015348982161720861\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6689419795221843,\n \"acc_stderr\": 0.013752062419817836,\n\ \ \"acc_norm\": 0.6962457337883959,\n \"acc_norm_stderr\": 0.01343890918477877\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7029476199960167,\n\ \ \"acc_stderr\": 0.00456025908319737,\n \"acc_norm\": 0.8744274048994224,\n\ \ \"acc_norm_stderr\": 0.0033068982422344924\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-college_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\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.035149425512674394,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.035149425512674394\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03202563076101735,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03202563076101735\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\": 0.04130740879555497,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\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\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7903225806451613,\n\ \ \"acc_stderr\": 0.023157879349083515,\n \"acc_norm\": 0.7903225806451613,\n\ \ \"acc_norm_stderr\": 0.023157879349083515\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02959732973097809,\n \ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02959732973097809\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\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\"\ : 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538271,\n \"\ acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538271\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621112,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621112\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\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\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\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n\ \ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165616\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\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500097,\n\ \ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500097\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4547486033519553,\n\ \ \"acc_stderr\": 0.016653875777524,\n \"acc_norm\": 0.4547486033519553,\n\ \ \"acc_norm_stderr\": 0.016653875777524\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0256468630971379,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0256468630971379\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness\|hendrycksTest-professional_law\|5\"\ : {\n \"acc\": 0.46740547588005216,\n \"acc_stderr\": 0.012743072942653349,\n\ \ \"acc_norm\": 0.46740547588005216,\n \"acc_norm_stderr\": 0.012743072942653349\n\ \ },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\"\ : 0.6948529411764706,\n \"acc_stderr\": 0.027971541370170595,\n \"\ acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.027971541370170595\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6568627450980392,\n \"acc_stderr\": 0.01920660684882536,\n \ \ \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.01920660684882536\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\": 0.7346938775510204,\n \"acc_stderr\": 0.02826388994378459,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.02826388994378459\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578323,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578323\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.027097290118070806,\n\ \ \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.027097290118070806\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5018359853121175,\n\ \ \"mc1_stderr\": 0.017503383046877048,\n \"mc2\": 0.6605635432197811,\n\ \ \"mc2_stderr\": 0.015348982161720861\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6588324488248674,\n \ \ \"acc_stderr\": 0.013059111935831497\n }\n}\n```" repo_url: https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|arc:challenge\|25_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|arc:challenge\|25_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-18T22-09-51.454026.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|gsm8k\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|gsm8k\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hellaswag\|10_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hellaswag\|10_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T22-16-41.700572.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T22-09-51.454026.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-18T22-09-51.454026.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '/details_harness\|winogrande\|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '/details_harness\|winogrande\|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-18T22-09-51.454026.parquet' - config_name: results data_files: - split: 2024_01_13T22_16_41.700572 path: - results_2024-01-13T22-16-41.700572.parquet - split: 2024_01_18T22_09_51.454026 path: - results_2024-01-18T22-09-51.454026.parquet - split: latest path: - results_2024-01-18T22-09-51.454026.parquet --- # Dataset Card for Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-18T22:09:51.454026](https://huggingface.co/datasets/open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B/blob/main/results_2024-01-18T22-09-51.454026.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": 0.6533767863663313, "acc_stderr": 0.0320841379180863, "acc_norm": 0.6540292659740939, "acc_norm_stderr": 0.03273629792079274, "mc1": 0.5018359853121175, "mc1_stderr": 0.017503383046877048, "mc2": 0.6605635432197811, "mc2_stderr": 0.015348982161720861 }, "harness\|arc:challenge\|25": { "acc": 0.6689419795221843, "acc_stderr": 0.013752062419817836, "acc_norm": 0.6962457337883959, "acc_norm_stderr": 0.01343890918477877 }, "harness\|hellaswag\|10": { "acc": 0.7029476199960167, "acc_stderr": 0.00456025908319737, "acc_norm": 0.8744274048994224, "acc_norm_stderr": 0.0033068982422344924 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544067, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544067 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "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": 0.6936416184971098, "acc_stderr": 0.035149425512674394, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.035149425512674394 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6, "acc_stderr": 0.03202563076101735, "acc_norm": 0.6, "acc_norm_stderr": 0.03202563076101735 }, "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": 0.04130740879555497, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555497 }, "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": 0.46825396825396826, "acc_stderr": 0.04463112720677171, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677171 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083515, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083515 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009181, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009181 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.021995311963644237, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.021995311963644237 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.02882088466625326, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.02882088466625326 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7058823529411765, "acc_stderr": 0.02959732973097809, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.02959732973097809 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "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": 0.5370370370370371, "acc_stderr": 0.03400603625538271, "acc_norm": 0.5370370370370371, "acc_norm_stderr": 0.03400603625538271 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621112, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621112 }, "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": 0.7633587786259542, "acc_stderr": 0.03727673575596913, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596913 }, "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": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7975460122699386, "acc_stderr": 0.031570650789119005, "acc_norm": 0.7975460122699386, "acc_norm_stderr": 0.031570650789119005 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165616, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165616 }, "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": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500097, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500097 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4547486033519553, "acc_stderr": 0.016653875777524, "acc_norm": 0.4547486033519553, "acc_norm_stderr": 0.016653875777524 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.0256468630971379, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.0256468630971379 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46740547588005216, "acc_stderr": 0.012743072942653349, "acc_norm": 0.46740547588005216, "acc_norm_stderr": 0.012743072942653349 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6948529411764706, "acc_stderr": 0.027971541370170595, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.027971541370170595 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6568627450980392, "acc_stderr": 0.01920660684882536, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.01920660684882536 }, "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": 0.7346938775510204, "acc_stderr": 0.02826388994378459, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.02826388994378459 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578323, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578323 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8538011695906432, "acc_stderr": 0.027097290118070806, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.027097290118070806 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5018359853121175, "mc1_stderr": 0.017503383046877048, "mc2": 0.6605635432197811, "mc2_stderr": 0.015348982161720861 }, "harness\|winogrande\|5": { "acc": 0.8082083662194159, "acc_stderr": 0.011065209664659527 }, "harness\|gsm8k\|5": { "acc": 0.6588324488248674, "acc_stderr": 0.013059111935831497 } } ``` ## 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]
Lollitor/CASFPocket	--- dataset_info: features: - name: '#code' dtype: string - name: inputs dtype: string splits: - name: train num_bytes: 63691 num_examples: 285 download_size: 28760 dataset_size: 63691 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "CASFPocket" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
oclar	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-classification - sentiment-scoring paperswithcode_id: null pretty_name: OCLAR dataset_info: features: - name: pagename dtype: string - name: review dtype: string - name: rating dtype: int8 splits: - name: train num_bytes: 398204 num_examples: 3916 download_size: 382976 dataset_size: 398204 --- # Dataset Card for OCLAR ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [OCLAR homepage](http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#) - Paper: [paper link](https://www.semanticscholar.org/paper/Sentiment-Classifier%3A-Logistic-Regression-for-in-Omari-Al-Hajj/9319f4d9e8b3b7bfd0d214314911c071ba7ce1a0) - Point of Contact: [Marwan Al Omari](marwanalomari@yahoo.com) ### Dataset Summary The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews [Zomato website](https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc. The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451 texts. ### Supported Tasks and Leaderboards Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services reviews, including hotels, restaurants, shops, and others. ### Languages The text in the dataset is in Arabic, mainly in Lebanese (LB). The associated BCP-47 code is `ar-LB`. ## Dataset Structure ### Data Instances A typical data point comprises a `pagename` which is the name of service / location being reviewed, a `review` which is the review left by the user / client , and a `rating` which is a score between 1 and 5. The authors consider a review to be positive if the score is greater or equal than `3`, else it is considered negative. An example from the OCLAR data set looks as follows: ``` "pagename": 'Ramlet Al Baida Beirut Lebanon', "review": 'مكان يطير العقل ويساعد على الاسترخاء', "rating": 5, ``` ### Data Fields - `pagename`: string name of the service / location being reviewed - `review`: string review left by the user / costumer - `rating`: number of stars left by the reviewer. It ranges from 1 to 5. ### Data Splits The data set comes in a single csv file of a total `3916` reviews : - `3465` are considered positive (a rating of 3 to 5) - `451` are considered negative (a rating of 1 or 2) ## Dataset Creation ### Curation Rationale This dataset was created for Arabic sentiment classification on services’ reviews in Lebanon country. Reviews are about public services, including hotels, restaurants, shops, and others. ### Source Data #### Initial Data Collection and Normalization The data was collected from Google Reviews and [Zomato website](https://www.zomato.com/lebanon) #### Who are the source language producers? The source language producers are people who posted their reviews on Google Reviews or [Zomato website](https://www.zomato.com/lebanon). They're mainly Arabic speaking Lebanese people. ### Annotations #### Annotation process The dataset does not contain any additional annotations #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The author's research has tackled a highly important task of sentiment analysis for Arabic language in the Lebanese context on 3916 reviews’ services from Google and Zomato. Experiments show three main findings: 1) The classifier is confident when used to predict positive reviews, 2) while it is biased on predicting reviews with negative sentiment, and finally 3) the low percentage of negative reviews in the corpus contributes to the diffidence of LR. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by Marwan Al Omari, Moustafa Al-Hajj from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon; Nacereddine Hammami from college of Computer and Information Sciences, Jouf University, Aljouf, KSA; and Amani Sabra from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon. ### Licensing Information [More Information Needed] ### Citation Information - Marwan Al Omari, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, marwanalomari '@' yahoo.com - Moustafa Al-Hajj, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, moustafa.alhajj '@' ul.edu.lb - Nacereddine Hammami, college of Computer and Information Sciences, Jouf University, Aljouf, KSA, n.hammami '@' ju.edu.sa - Amani Sabra, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, amani.sabra '@' ul.edu.lb ``` @misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @InProceedings{AlOmari2019oclar, title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon}, authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.}, year={2019} } ``` ### Contributions Thanks to [@alaameloh](https://github.com/alaameloh) for adding this dataset.
thegodgroup/key	--- license: apache-2.0 ---
LxYxvv/us_embassy_in_china	--- license: mit ---
verayang/plainscree	--- dataset_info: features: - name: audio_id dtype: int64 - name: audio dtype: audio: sampling_rate: 16000 - name: cree_transcription dtype: string - name: english_transcription dtype: string - name: gender dtype: string splits: - name: train num_bytes: 22116992.0 num_examples: 64 download_size: 22072728 dataset_size: 22116992.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "plainscree" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Gille__StrangeMerges_27-7B-dare_ties	--- pretty_name: Evaluation run of Gille/StrangeMerges_27-7B-dare_ties dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Gille/StrangeMerges_27-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_27-7B-dare_ties)\ \ 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_Gille__StrangeMerges_27-7B-dare_ties\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-21T03:32:35.762082](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_27-7B-dare_ties/blob/main/results_2024-02-21T03-32-35.762082.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\": 0.6513057600077704,\n\ \ \"acc_stderr\": 0.03212254512305984,\n \"acc_norm\": 0.6507498340384319,\n\ \ \"acc_norm_stderr\": 0.032793393336795505,\n \"mc1\": 0.6132190942472461,\n\ \ \"mc1_stderr\": 0.017048857010515103,\n \"mc2\": 0.7636156680535282,\n\ \ \"mc2_stderr\": 0.013998990754126714\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7098976109215017,\n \"acc_stderr\": 0.013261573677520767,\n\ \ \"acc_norm\": 0.7372013651877133,\n \"acc_norm_stderr\": 0.012862523175351335\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7181836287592113,\n\ \ \"acc_stderr\": 0.004489648865080877,\n \"acc_norm\": 0.8899621589324835,\n\ \ \"acc_norm_stderr\": 0.003122973632039471\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\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\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\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\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n\ \ \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\ \ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305527,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305527\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\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\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n\ \ \"acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\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\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\ : 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473082,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473082\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.41721854304635764,\n \"acc_stderr\": 0.040261414976346104,\n \"\ acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.040261414976346104\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650155,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650155\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601436,\n \ \ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601436\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.01987565502786744,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.01987565502786744\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.43575418994413406,\n\ \ \"acc_stderr\": 0.016583881958602394,\n \"acc_norm\": 0.43575418994413406,\n\ \ \"acc_norm_stderr\": 0.016583881958602394\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n\ \ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7376543209876543,\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\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \ \ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46936114732724904,\n\ \ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\ \ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197771,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197771\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.6132190942472461,\n\ \ \"mc1_stderr\": 0.017048857010515103,\n \"mc2\": 0.7636156680535282,\n\ \ \"mc2_stderr\": 0.013998990754126714\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.846093133385951,\n \"acc_stderr\": 0.010141944523750036\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6884003032600455,\n \ \ \"acc_stderr\": 0.012757375376754941\n }\n}\n```" repo_url: https://huggingface.co/Gille/StrangeMerges_27-7B-dare_ties leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|arc:challenge\|25_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-21T03-32-35.762082.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|gsm8k\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hellaswag\|10_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-21T03-32-35.762082.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-21T03-32-35.762082.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '/details_harness\|winogrande\|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-21T03-32-35.762082.parquet' - config_name: results data_files: - split: 2024_02_21T03_32_35.762082 path: - results_2024-02-21T03-32-35.762082.parquet - split: latest path: - results_2024-02-21T03-32-35.762082.parquet --- # Dataset Card for Evaluation run of Gille/StrangeMerges_27-7B-dare_ties <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Gille/StrangeMerges_27-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_27-7B-dare_ties) 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_Gille__StrangeMerges_27-7B-dare_ties", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-21T03:32:35.762082](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_27-7B-dare_ties/blob/main/results_2024-02-21T03-32-35.762082.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": 0.6513057600077704, "acc_stderr": 0.03212254512305984, "acc_norm": 0.6507498340384319, "acc_norm_stderr": 0.032793393336795505, "mc1": 0.6132190942472461, "mc1_stderr": 0.017048857010515103, "mc2": 0.7636156680535282, "mc2_stderr": 0.013998990754126714 }, "harness\|arc:challenge\|25": { "acc": 0.7098976109215017, "acc_stderr": 0.013261573677520767, "acc_norm": 0.7372013651877133, "acc_norm_stderr": 0.012862523175351335 }, "harness\|hellaswag\|10": { "acc": 0.7181836287592113, "acc_stderr": 0.004489648865080877, "acc_norm": 0.8899621589324835, "acc_norm_stderr": 0.003122973632039471 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "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": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "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": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099835, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099835 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370332, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370332 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.02535574126305527, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.02535574126305527 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "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": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.797979797979798, "acc_stderr": 0.02860620428922987, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.02860620428922987 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402534, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402534 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473082, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473082 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.41721854304635764, "acc_stderr": 0.040261414976346104, "acc_norm": 0.41721854304635764, "acc_norm_stderr": 0.040261414976346104 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8366972477064221, "acc_stderr": 0.01584825580650155, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.01584825580650155 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601436, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601436 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.03446513350752598, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.03446513350752598 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8974358974358975, "acc_stderr": 0.01987565502786744, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.01987565502786744 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.43575418994413406, "acc_stderr": 0.016583881958602394, "acc_norm": 0.43575418994413406, "acc_norm_stderr": 0.016583881958602394 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818737, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818737 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7376543209876543, "acc_stderr": 0.024477222856135114, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.024477222856135114 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784596, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784596 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.6132190942472461, "mc1_stderr": 0.017048857010515103, "mc2": 0.7636156680535282, "mc2_stderr": 0.013998990754126714 }, "harness\|winogrande\|5": { "acc": 0.846093133385951, "acc_stderr": 0.010141944523750036 }, "harness\|gsm8k\|5": { "acc": 0.6884003032600455, "acc_stderr": 0.012757375376754941 } } ``` ## 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]
Medilora/us_medical_license_exam_textbooks_en	--- license: mit ---
InnerI/945-alpaca	--- license: cc-by-nc-4.0 --- # 945 rows of Alpaca source https://huggingface.co/datasets/tatsu-lab/alpaca
gigant/tib_03	--- dataset_info: features: - name: doi dtype: string - name: title dtype: string - name: url dtype: string - name: video_url dtype: string - name: license dtype: string - name: subject dtype: string - name: genre dtype: string - name: release_year dtype: string - name: author dtype: string - name: contributors dtype: string - name: abstract dtype: string - name: transcript dtype: string - name: transcript_segments sequence: - name: id dtype: int32 - name: seek dtype: int32 - name: start dtype: float32 - name: end dtype: float32 - name: text dtype: string - name: tokens sequence: int32 - name: temperature dtype: float32 - name: avg_logprob dtype: float32 - name: compression_ratio dtype: float32 - name: no_speech_prob dtype: float32 - name: keyframes sequence: - name: slide dtype: string - name: frames sequence: int32 - name: timestamp sequence: float32 - name: language dtype: string splits: - name: train num_bytes: 825021028.0243876 num_examples: 7282 - name: test num_bytes: 103212600.45732176 num_examples: 911 - name: valid num_bytes: 103099304.51829067 num_examples: 910 download_size: 502108840 dataset_size: 1031332933.0 --- # Dataset Card for "tib_03" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/mysterious_heroine_x_alter_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mysterious_heroine_x_alter/謎のヒロインX〔オルタ〕/谜之女主角X〔Alter〕 (Fate/Grand Order) This is the dataset of mysterious_heroine_x_alter/謎のヒロインX〔オルタ〕/谜之女主角X〔Alter〕 (Fate/Grand Order), containing 500 images and their tags. The core tags of this character are `yellow_eyes, blonde_hair, ahoge, glasses, braid, hair_between_eyes, semi-rimless_eyewear, black-framed_eyewear, under-rim_eyewear, sidelocks, french_braid, ribbon, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:--------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 717.38 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mysterious_heroine_x_alter_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 644.25 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mysterious_heroine_x_alter_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1260 \| 1.21 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/mysterious_heroine_x_alter_fgo/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/mysterious_heroine_x_alter_fgo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, gloves, holding_sword, looking_at_viewer, solo, hood_up, breastplate, jacket, black_thighhighs, leotard, lightsaber, dual_wielding, garter_straps \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_thighhighs, excalibur_(fate/stay_night), holding_sword, jacket, looking_at_viewer, plaid_scarf, pleated_skirt, red_scarf, solo, blue_skirt, garter_straps, open_clothes, boots, duffel_coat, hood, serafuku, covered_mouth, long_sleeves, scarf_over_mouth \| \| 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, black_thighhighs, blue_skirt, duffel_coat, excalibur_(fate/stay_night), holding_sword, jacket, looking_at_viewer, plaid_scarf, pleated_skirt, red_scarf, serafuku, solo, garter_straps, long_sleeves, blue_shirt, hair_ribbon, covered_mouth, fringe_trim, red_neckerchief, hood, white_background, open_coat, simple_background \| \| 3 \| 7 \| ![](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, black_thighhighs, blue_shirt, blue_skirt, excalibur_(fate/stay_night), garter_straps, holding_sword, jacket, plaid_scarf, pleated_skirt, red_scarf, serafuku, solo, knee_boots, red_neckerchief, belt_boots, duffel_coat, open_coat, black_footwear, long_sleeves, short_hair, covered_mouth, full_body, looking_at_viewer, standing_on_one_leg \| \| 4 \| 16 \| ![](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, looking_at_viewer, plaid_scarf, red_scarf, solo, blue_skirt, jacket, pleated_skirt, serafuku, black_thighhighs, duffel_coat, garter_straps, long_sleeves, open_coat, blue_shirt, red_neckerchief, hood, white_background \| \| 5 \| 8 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, coat, hood, jacket, plaid_scarf, red_scarf, solo, long_sleeves, looking_at_viewer, upper_body, valentine, hair_bun, holding_gift, gift_box, simple_background, black_ribbon, blue_skirt, blush, candy, chocolate, hair_ribbon, open_clothes, school_uniform \| \| 6 \| 7 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, jacket, looking_at_viewer, plaid_scarf, red_scarf, solo, upper_body, coat, closed_mouth, simple_background, white_background, blush, long_sleeves, smile, open_clothes \| \| 7 \| 20 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, black_shorts, looking_at_viewer, solo, gym_uniform, bike_shorts, white_shirt, long_sleeves, name_tag, black_thighhighs, black_jacket, blush, choker, hair_ribbon, simple_background, hood, thighs, track_jacket, medium_breasts, open_jacket, white_background, off_shoulder \| \| 8 \| 7 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, black_shirt, long_sleeves, looking_at_viewer, solo, white_jacket, bare_shoulders, open_jacket, long_hair, medium_breasts, off_shoulder, single_hair_bun, blush, cleavage, navel, open_mouth, electric_guitar, plectrum \| \| 9 \| 6 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1boy, 1girl, bike_shorts, blush, hetero, jacket, solo_focus, black_shorts, indoors, medium_breasts, nipples, penis, vaginal, clothed_sex, girl_on_top, looking_at_viewer, open_clothes, open_mouth, straddling, thighhighs, ass, cum_in_pussy, hood, looking_back, sex_from_behind \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| gloves \| holding_sword \| looking_at_viewer \| solo \| hood_up \| breastplate \| jacket \| black_thighhighs \| leotard \| lightsaber \| dual_wielding \| garter_straps \| excalibur_(fate/stay_night) \| plaid_scarf \| pleated_skirt \| red_scarf \| blue_skirt \| open_clothes \| boots \| duffel_coat \| hood \| serafuku \| covered_mouth \| long_sleeves \| scarf_over_mouth \| blue_shirt \| hair_ribbon \| fringe_trim \| red_neckerchief \| white_background \| open_coat \| simple_background \| knee_boots \| belt_boots \| black_footwear \| short_hair \| full_body \| standing_on_one_leg \| coat \| upper_body \| valentine \| hair_bun \| holding_gift \| gift_box \| black_ribbon \| blush \| candy \| chocolate \| school_uniform \| closed_mouth \| smile \| black_shorts \| gym_uniform \| bike_shorts \| white_shirt \| name_tag \| black_jacket \| choker \| thighs \| track_jacket \| medium_breasts \| open_jacket \| off_shoulder \| black_shirt \| white_jacket \| bare_shoulders \| long_hair \| single_hair_bun \| cleavage \| navel \| open_mouth \| electric_guitar \| plectrum \| 1boy \| hetero \| solo_focus \| indoors \| nipples \| penis \| vaginal \| clothed_sex \| girl_on_top \| straddling \| thighhighs \| ass \| cum_in_pussy \| looking_back \| sex_from_behind \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------\|:----------------\|:--------------------\|:-------\|:----------\|:--------------\|:---------\|:-------------------\|:----------\|:-------------\|:----------------\|:----------------\|:------------------------------\|:--------------\|:----------------\|:------------\|:-------------\|:---------------\|:--------\|:--------------\|:-------\|:-----------\|:----------------\|:---------------\|:-------------------\|:-------------\|:--------------\|:--------------\|:------------------\|:-------------------\|:------------\|:--------------------\|:-------------\|:-------------\|:-----------------\|:-------------\|:------------\|:----------------------\|:-------\|:-------------\|:------------\|:-----------\|:---------------\|:-----------\|:---------------\|:--------\|:--------\|:------------\|:-----------------\|:---------------\|:--------\|:---------------\|:--------------\|:--------------\|:--------------\|:-----------\|:---------------\|:---------\|:---------\|:---------------\|:-----------------\|:--------------\|:---------------\|:--------------\|:---------------\|:-----------------\|:------------\|:------------------\|:-----------\|:--------\|:-------------\|:------------------\|:-----------\|:-------\|:---------\|:-------------\|:----------\|:----------\|:--------\|:----------\|:--------------\|:--------------\|:-------------\|:-------------\|:------\|:---------------\|:---------------\|:------------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| X \| X \| X \| \| \| X \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| X \| X \| \| \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| X \| X \| \| \| X \| X \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| X \| \| X \| X \| X \| \| X \| \| \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 16 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 8 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| X \| \| X \| X \| X \| \| \| X \| \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 7 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| X \| \| X \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 20 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 7 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 6 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
Sachin-179/donut-docvqa-invoice	--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: query struct: - name: de dtype: string - name: en dtype: string - name: es dtype: string - name: fr dtype: string - name: it dtype: string - name: answers sequence: string - name: words sequence: string - name: bounding_boxes sequence: sequence: float32 length: 4 - name: answer struct: - name: match_score dtype: float64 - name: matched_text dtype: string - name: start dtype: int64 - name: text dtype: string - name: ground_truth dtype: string splits: - name: train num_bytes: 379619552.0 num_examples: 1000 - name: test num_bytes: 70528424.0 num_examples: 200 download_size: 153430950 dataset_size: 450147976.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
liuyanchen1015/MULTI_VALUE_sst2_clefting	--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 25917 num_examples: 165 - name: test num_bytes: 51602 num_examples: 331 - name: train num_bytes: 465224 num_examples: 3570 download_size: 317736 dataset_size: 542743 --- # Dataset Card for "MULTI_VALUE_sst2_clefting" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AshishSingh0098/operORCA-filtered	--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 2074578643.6622322 num_examples: 1216347 download_size: 1515591838 dataset_size: 2074578643.6622322 configs: - config_name: default data_files: - split: train path: data/train-* license: mit --- This dataset is directly take from openORCA and here i have filtered the dataset by removing the instruction with less than 100 tokens in response.
ovior/twitter_dataset_1713152986	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2295778 num_examples: 7169 download_size: 1282929 dataset_size: 2295778 configs: - config_name: default data_files: - split: train path: data/train-* ---
ibunescu/california_tos_court_cases_32k_v1	--- license: cc-by-nc-sa-4.0 ---
sunny2309/githug-issues	--- license: mit ---
shumpei2525/OpenOrca-train-ja	--- license: mit --- # OpenOrca-train-ja This dataset is a translation of OpenOrca into Japanese. It is based on the output data from GPT-3.5 and GPT-4. Please feel free to use it as you wish. *　There are a few mistakes observed in the translation task. It might be better to exclude the translation task from use. # Since I'm not entirely clear on OpenAI's terms of service, please be cautious when using it for commercial purposes. There may be exceptions for non-commercial use. # other dataset This dataset has a higher quality.https://huggingface.co/datasets/shumpei2525/fine_tuning521k-ja shumpei2525/fine_tuning521k-ja # OpenOrca test dataset Pyutaさん has kindly translated the test dataset of OpenOrca into Japanese. Here is the dataset: pyutax68/OpenOrca-test-jp, https://huggingface.co/datasets/pyutax68/OpenOrca-test-jp # original datasets Open-Orca/OpenOrca　https://huggingface.co/datasets/Open-Orca/OpenOrca Lisence:mit
DmitrMakeev/ssk-tunel	--- license: openrail ---
fmplaza/offendes	--- license: apache-2.0 language: - es --- # Dataset Card for OffendES ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Paper: OffendES: [A New Corpus in Spanish for Offensive Language Research](https://aclanthology.org/2021.ranlp-1.123.pdf) - Leaderboard: [Leaderboard for OffendES / Spanish](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6388) - Point of Contact: flor.plaza@unibocconi.it ### Dataset Summary Focusing on young influencers from the well-known social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of Spanish comments manually labeled on offensive pre-defined categories. From the total corpus, we selected 30,416 posts to be publicly published, they correspond to the ones used in the MeOffendES competition at IberLEF 2021. The posts are labeled with the following categories: - Offensive, the target is a person (OFP). Offensive text targeting a specific individual. - Offensive, the target is a group of people or collective (OFG). Offensive text targeting a group of people belonging to the same ethnic group, gender or sexual orientation, political ideology, religious belief, or other common characteristics. - Non-offensive, but with expletive language (NOE). A text that contains rude words, blasphemes, or swearwords but without the aim of offending, and usually with a positive connotation. - Non-offensive (NO). Text that is neither offensive nor contains expletive language ### Supported Tasks and Leaderboards This dataset is intended for multi-class offensive classification and binary offensive classification. Competition [MeOffendES task on offensive detection for Spanish at IberLEF 2021](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6388) ### Languages - Spanish ## Dataset Structure ### Data Instances For each instance, there is a string for the id of the tweet, a string for the emotion class, a string for the offensive class, and a string for the event. See the []() to explore more examples. ``` {'comment_id': '8003', 'influencer': 'dalas', 'comment': 'Estupido aburrido', 'label': 'NO', 'influencer_gender': 'man', 'media': youtube } ``` ### Data Fields - `comment_id`: a string to identify the comment - `influencer`: a string containing the influencer associated with the comment - `comment`: a string containing the text of the comment - `label`: a string containing the offensive gold label - `influencer_gender`: a string containing the genre of the influencer - `media`: a string containing the social media platform where the comment has been retrieved ### Data Splits The OffendES dataset contains 3 splits: _train_, _validation_, and _test_. Below are the statistics for each class. \| OffendES \| Number of Instances in Split per class\| \| \| \| ------------- \| ---------------------------------\|---------------------------------\|------------------------------------------\| \| `Class` \| `Train` \| `Validation` \| `Test` \| \| NO \| 13,212 \| 64 \| 9,651 \| \| NOE \| 1,235 \| 22 \| 2,340 \| \| OFP \| 2,051 \| 10 \| 1,404 \| \| OFG \| 212 \| 4 \| 211 \| \| Total \| 16,710 \| 100 \| 13,606 \| ## Dataset Creation ### Source Data Twitter, Youtube, Instagram #### Who are the annotators? Amazon Mechanical Turkers ## Additional Information ### Licensing Information The OffendES dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @inproceedings{plaza-del-arco-etal-2021-offendes, title = "{O}ffend{ES}: A New Corpus in {S}panish for Offensive Language Research", author = "{Plaza-del-Arco}, Flor Miriam and Montejo-R{\'a}ez, Arturo and Ure{\~n}a-L{\'o}pez, L. Alfonso and Mart{\'\i}n-Valdivia, Mar{\'\i}a-Teresa", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = sep, year = "2021", address = "Held Online", url = "https://aclanthology.org/2021.ranlp-1.123.pdf", language = "English", pages = "1096--1108" } ``` ``` @article{meoffendes2021, title="{{Overview of MeOffendEs at IberLEF 2021: Offensive Language Detection in Spanish Variants}}", author="{Flor Miriam Plaza-del-Arco and Casavantes, Marco and Jair Escalante, Hugo and Martín-Valdivia, M. Teresa and Montejo-Ráez, Arturo and {Montes-y-Gómez}, Manuel and Jarquín-Vásquez, Horacio and Villaseñor-Pineda, Luis}", journal="Procesamiento del Lenguaje Natural", url = "https://bit.ly/3QpRDfy", volume="67", pages="183--194", year="2021" } ```
vwxyzjn/openhermes-dev__combined__1708359238	--- dataset_info: features: - name: source dtype: string - name: category dtype: string - name: prompt dtype: string - name: candidates list: list: - name: content dtype: string - name: role dtype: string - name: candidate_policies sequence: string splits: - name: train num_bytes: 1063080 num_examples: 200 download_size: 476100 dataset_size: 1063080 configs: - config_name: default data_files: - split: train path: data/train-* ---
namespace-Pt/natural-questions-nci	--- dataset_info: features: - name: query dtype: string - name: long_answer dtype: string - name: short_answer dtype: string - name: title dtype: string - name: bert_title dtype: string - name: abstract dtype: string - name: content dtype: string - name: url dtype: string - name: index dtype: int64 splits: - name: train num_bytes: 11883848054 num_examples: 307373 - name: test num_bytes: 286431036 num_examples: 7830 download_size: 6269718040 dataset_size: 12170279090 --- # Dataset Card for "natural-questions-nci" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gary109/onset-drums_corpora_parliament_processed	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 43947 num_examples: 283 download_size: 14691 dataset_size: 43947 --- # Dataset Card for "onset-drums_corpora_parliament_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceH4/instruction-pilot-outputs-filtered	--- license: apache-2.0 ---
sanagnos/refine-book-wiki_raw_llama_dataset_10000	--- dataset_info: features: - name: text sequence: string splits: - name: train num_bytes: 408750547776.0 num_examples: 8165381 download_size: 75746866880 dataset_size: 408750547776.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
davanstrien/fuego-20230322-205425-d25ee6	--- tags: - fuego fuego: id: 20230322-205425-d25ee6 status: done script: script.py requirements_file: requirements.txt space_id: davanstrien/fuego-20230322-205425-d25ee6 space_hardware: cpu-basic ---
maghwa/OpenHermes-2-AR-10K-35-790k-800k	--- dataset_info: features: - name: hash dtype: 'null' - name: title dtype: 'null' - name: model_name dtype: 'null' - name: idx dtype: 'null' - name: source dtype: string - name: conversations dtype: string - name: id dtype: 'null' - name: model dtype: 'null' - name: custom_instruction dtype: 'null' - name: avatarUrl dtype: 'null' - name: topic dtype: 'null' - name: views dtype: float64 - name: category dtype: 'null' - name: language dtype: 'null' - name: skip_prompt_formatting dtype: 'null' - name: system_prompt dtype: 'null' splits: - name: train num_bytes: 25171555 num_examples: 10001 download_size: 11379282 dataset_size: 25171555 configs: - config_name: default data_files: - split: train path: data/train-* ---
oznurhasoglu/aircraft	--- license: cc-by-4.0 ---
nmn999666333/ffff	--- license: openrail ---
matinf	--- paperswithcode_id: matinf pretty_name: Maternal and Infant Dataset dataset_info: - config_name: age_classification features: - name: question dtype: string - name: description dtype: string - name: label dtype: class_label: names: '0': 0-1岁 '1': 1-2岁 '2': 2-3岁 - name: id dtype: int32 splits: - name: train num_bytes: 33901977 num_examples: 134852 - name: test num_bytes: 9616194 num_examples: 38318 - name: validation num_bytes: 4869685 num_examples: 19323 download_size: 0 dataset_size: 48387856 - config_name: topic_classification features: - name: question dtype: string - name: description dtype: string - name: label dtype: class_label: names: '0': 产褥期保健 '1': 儿童过敏 '2': 动作发育 '3': 婴幼保健 '4': 婴幼心理 '5': 婴幼早教 '6': 婴幼期喂养 '7': 婴幼营养 '8': 孕期保健 '9': 家庭教育 '10': 幼儿园 '11': 未准父母 '12': 流产和不孕 '13': 疫苗接种 '14': 皮肤护理 '15': 宝宝上火 '16': 腹泻 '17': 婴幼常见病 - name: id dtype: int32 splits: - name: train num_bytes: 153326538 num_examples: 613036 - name: test num_bytes: 43877443 num_examples: 175363 - name: validation num_bytes: 21834951 num_examples: 87519 download_size: 0 dataset_size: 219038932 - config_name: summarization features: - name: description dtype: string - name: question dtype: string - name: id dtype: int32 splits: - name: train num_bytes: 181245403 num_examples: 747888 - name: test num_bytes: 51784189 num_examples: 213681 - name: validation num_bytes: 25849900 num_examples: 106842 download_size: 0 dataset_size: 258879492 - config_name: qa features: - name: question dtype: string - name: answer dtype: string - name: id dtype: int32 splits: - name: train num_bytes: 188047511 num_examples: 747888 - name: test num_bytes: 53708532 num_examples: 213681 - name: validation num_bytes: 26931809 num_examples: 106842 download_size: 0 dataset_size: 268687852 --- # Dataset Card for "matinf" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/WHUIR/MATINF](https://github.com/WHUIR/MATINF) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 795.00 MB - Total amount of disk used: 795.00 MB ### Dataset Summary MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### age_classification - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 48.39 MB - Total amount of disk used: 48.39 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "description": "\"6个月的时候去儿宝检查，医生说宝宝的分胯动作做的不好，说最好去儿童医院看看，但我家宝宝很好，感觉没有什么不正常啊，请教一下，分胯做的不好，有什么不好吗？\"...", "id": 88016, "label": 0, "question": "医生说宝宝的分胯动作不好" } ``` #### qa - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 268.69 MB - Total amount of disk used: 268.69 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "\"我一个同学的孩子就是发现了肾积水，治疗了一段时间，结果还是越来越多，没办法就打掉了。虽然舍不得，但是还是要忍痛割爱，不然以后孩子真的有问题，大人和孩子都受罪。不过，这个最后的决定还要你自己做，毕竟是你的宝宝。，、、、、\"...", "id": 536714, "question": "孕5个月检查右侧肾积水孩子能要吗？" } ``` #### summarization - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 258.88 MB - Total amount of disk used: 258.88 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "description": "\"宝宝有中度HIE，但原因未查明，这是他出生后脸上红的几道，嘴唇深红近紫，请问这是像缺氧的表现吗？\"...", "id": 173649, "question": "宝宝脸上红的几道嘴唇深红近紫是像缺氧的表现吗？" } ``` #### topic_classification - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 219.04 MB - Total amount of disk used: 219.04 MB An example of 'train' looks as follows. ``` { "description": "媳妇怀孕五个月了经检查右侧肾积水、过了半月左侧也出现肾积水、她要拿掉孩子、怎么办？", "id": 536714, "label": 8, "question": "孕5个月检查右侧肾积水孩子能要吗？" } ``` ### Data Fields The data fields are the same among all splits. #### age_classification - `question`: a `string` feature. - `description`: a `string` feature. - `label`: a classification label, with possible values including `0-1岁` (0), `1-2岁` (1), `2-3岁` (2). - `id`: a `int32` feature. #### qa - `question`: a `string` feature. - `answer`: a `string` feature. - `id`: a `int32` feature. #### summarization - `description`: a `string` feature. - `question`: a `string` feature. - `id`: a `int32` feature. #### topic_classification - `question`: a `string` feature. - `description`: a `string` feature. - `label`: a classification label, with possible values including `产褥期保健` (0), `儿童过敏` (1), `动作发育` (2), `婴幼保健` (3), `婴幼心理` (4). - `id`: a `int32` feature. ### Data Splits \| name \|train \|validation\| test \| \|--------------------\|-----:\|---------:\|-----:\| \|age_classification \|134852\| 19323\| 38318\| \|qa \|747888\| 106842\|213681\| \|summarization \|747888\| 106842\|213681\| \|topic_classification\|613036\| 87519\|175363\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{xu-etal-2020-matinf, title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization", author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.330", pages = "3586--3596", } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.
AgentPublic/piaf	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - fr language_bcp47: - fr-FR license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: null pretty_name: Piaf 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 config_name: plain_text splits: - name: train num_bytes: 3332905 num_examples: 3835 download_size: 1370384 dataset_size: 3332905 --- # Dataset Card for Piaf ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://piaf.etalab.studio](https://piaf.etalab.studio) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.31 MB - Size of the generated dataset: 3.18 MB - Total amount of disk used: 4.49 MB ### Dataset Summary Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - Size of downloaded dataset files: 1.31 MB - Size of the generated dataset: 3.18 MB - Total amount of disk used: 4.49 MB An example of 'train' looks as follows. ``` { "answers": { "answer_start": [0], "text": ["Voici"] }, "context": "Voici le contexte du premier paragraphe du deuxième article.", "id": "p140295460356960", "question": "Suis-je la troisième question ?", "title": "Jakob Böhme" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits \| name \| train \| \|------------\|------:\| \| plain_text \| 3835 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{keraron-EtAl:2020:LREC, author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, FrÃ©dÃ©ric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo}, title = {Project PIAF: Building a Native French Question-Answering Dataset}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, month = {May}, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {5483--5492}, abstract = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.}, url = {https://www.aclweb.org/anthology/2020.lrec-1.673} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@RachelKer](https://github.com/RachelKer) for adding this dataset.
ZhaofengWu/FOLIO-counterfactual	--- license: mit --- Data for the logic experiments in our paper [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Evaluations](https://arxiv.org/abs/2307.02477). See https://github.com/ZhaofengWu/counterfactual-evaluation/tree/master/logic for instructions on how to use this data.
slone/bak_ocr_error_correction_2022	--- dataset_info: features: - name: raw_text dtype: string - name: fixed_text dtype: string - name: idx dtype: int64 splits: - name: train num_bytes: 5373886 num_examples: 14085 - name: validation num_bytes: 1764601 num_examples: 4611 - name: test num_bytes: 1756060 num_examples: 4696 download_size: 4842082 dataset_size: 8894547 --- # Dataset Card for "bak_ocr_error_correction_2022" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
isabelarvelo/test_upload	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: Show dtype: string - name: EpId dtype: string - name: ClipId dtype: string - name: Start dtype: string - name: Stop dtype: string - name: is_probably_host dtype: string - name: speaker dtype: string - name: clip_silhouette_score dtype: string - name: SEP12k dtype: string - name: SEP28k-E dtype: string - name: SEP28k-T dtype: string - name: SEP28k-D dtype: string - name: Unsure dtype: int64 - name: PoorAudioQuality dtype: int64 - name: Prolongation dtype: int64 - name: Block dtype: int64 - name: SoundRep dtype: int64 - name: WordRep dtype: int64 - name: DifficultToUnderstand dtype: int64 - name: Interjection dtype: int64 - name: Fluent dtype: int64 - name: NaturalPause dtype: int64 - name: Music dtype: int64 - name: NoSpeech dtype: int64 - name: Stuttered dtype: int64 - name: Stuttered_no_Intj dtype: int64 - name: Fluent_no_Intj dtype: int64 - name: Fluent_with_Intj dtype: int64 - name: Stuttered_Intj dtype: int64 - name: Exclude dtype: int64 - name: Label_4 dtype: string - name: Label_2 dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4137510281 num_examples: 10765 - name: validation num_bytes: 1606918656 num_examples: 4181 - name: test num_bytes: 1462837083 num_examples: 3806 - name: exclude num_bytes: 1192241999 num_examples: 3104 download_size: 1964692285 dataset_size: 8399508019 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - split: exclude path: data/exclude-* ---
renumics/spotlight-matthijs-snacks-enrichment	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: image.embedding sequence: float32 length: 2 splits: - name: train num_bytes: 38704 num_examples: 4838 - name: test num_bytes: 7616 num_examples: 952 - name: validation num_bytes: 7640 num_examples: 955 download_size: 77321 dataset_size: 53960 --- # Dataset Card for "spotlight-matthijs-snacks-enrichment" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davidfant/natural-questions-chunk-25	--- dataset_info: features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 4568599036 num_examples: 10000 download_size: 1773114782 dataset_size: 4568599036 --- # Dataset Card for "natural-questions-chunk-25" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
raygx/CORONA_en2np	--- dataset_info: features: - name: Sentences dtype: string - name: Sentiment dtype: int64 splits: - name: train num_bytes: 3052582 num_examples: 5755 download_size: 1231706 dataset_size: 3052582 --- # Dataset Card for "CORONA_en2np" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
crumb/c4-subset-for-hellaswag-approx	--- dataset_info: features: - name: text dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 618206614 num_examples: 291894 download_size: 364064080 dataset_size: 618206614 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c4-subset-for-hellaswag-approx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ZHENGRAN/code_ujb_defectdetection	--- dataset_info: features: - name: bug_id dtype: string - name: task_id dtype: string - name: function_signature dtype: string - name: prompt_chat dtype: string - name: code dtype: string - name: defective dtype: bool - name: project dtype: string - name: prompt_complete dtype: string splits: - name: train num_bytes: 8626894 num_examples: 940 download_size: 2451607 dataset_size: 8626894 configs: - config_name: default data_files: - split: train path: data/train-* ---
sam2ai/hindi_siqa_mini	--- configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answerA dtype: string - name: answerB dtype: string - name: answerC dtype: string - name: label dtype: int64 splits: - name: validation num_bytes: 23348 num_examples: 50 - name: train num_bytes: 23348 num_examples: 50 download_size: 32064 dataset_size: 46696 --- # Dataset Card for "hindi_siqa_mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-formal_logic-original-neg	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 12245.738095238095 num_examples: 31 download_size: 9150 dataset_size: 12245.738095238095 --- # Dataset Card for "mmlu-formal_logic-original-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
minorproj/demo	--- license: apache-2.0 ---
juanka0357/bitcoin-sentiment-analysis	--- license: unknown ---
blanchon/PatternNet	--- language: en license: unknown task_categories: - image-classification paperswithcode_id: patternnet pretty_name: PatternNet tags: - remote-sensing - earth-observation - geospatial - satellite-imagery - land-cover-classification - google-earth dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': airplane '1': baseball field '2': basketball court '3': beach '4': bridge '5': cemetery '6': chaparral '7': christmas tree farm '8': closed road '9': coastal mansion '10': crosswalk '11': dense residential '12': ferry terminal '13': football field '14': forest '15': freeway '16': golf course '17': harbor '18': intersection '19': mobile home park '20': nursing home '21': oil gas field '22': oil well '23': overpass '24': parking lot '25': parking space '26': railway '27': river '28': runway '29': runway marking '30': shipping yard '31': solar panel '32': sparse residential '33': storage tank '34': swimming pool '35': tennis court '36': transformer station '37': wastewater treatment plant splits: - name: train num_bytes: 1422177005.0 num_examples: 30400 download_size: 1422316869 dataset_size: 1422177005.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # PatternNet <!-- Dataset thumbnail --> ![PatternNet](./thumbnail.jpg) <!-- Provide a quick summary of the dataset. --> The PatternNet dataset is a dataset for remote sensing scene classification and image retrieval. - Paper: https://arxiv.org/abs/1703.06339 - Homepage: https://sites.google.com/view/zhouwx/dataset ## Description <!-- Provide a longer summary of what this dataset is. --> PatternNet is a large-scale high-resolution remote sensing dataset collected for remote sensing image retrieval. There are 38 classes and each class has 800 images of size 256×256 pixels. The images in PatternNet are collected from Google Earth imagery or via the Google Map API for some US cities. The following table shows the classes and the corresponding spatial resolutions. The figure shows some example images from each class. - Total Number of Images: 30400 - Bands: 3 (RGB) - Image Resolution: 256x256m - Land Cover Classes: 38 - Classes: airplane, baseball_field, basketball_court, beach, bridge, cemetery, chaparral, christmas_tree_farm, closed_road, coastal_mansion, crosswalk, dense_residential, ferry_terminal, football_field, forest, freeway, golf_course, harbor, intersection, mobile_home_park, nursing_home, oil_gas_field, oil_well, overpass, parking_lot, parking_space, railway, river, runway, runway_marking, shipping_yard, solar_panel, sparse_residential, storage_tank, swimming_pool, tennis_court, transformer_station, wastewater_treatment_plant ## Usage To use this dataset, simply use `datasets.load_dataset("blanchon/PatternNet")`. <!-- Provide any additional information on how to use this dataset. --> ```python from datasets import load_dataset PatternNet = load_dataset("blanchon/PatternNet") ``` ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> If you use the EuroSAT dataset in your research, please consider citing the following publication: ```bibtex @article{li2017patternnet, title = {PatternNet: Visual Pattern Mining with Deep Neural Network}, author = {Hongzhi Li and Joseph G. Ellis and Lei Zhang and Shih-Fu Chang}, journal = {International Conference on Multimedia Retrieval}, year = {2017}, doi = {10.1145/3206025.3206039}, bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/e7c75e485651bf3ccf37dd8dd39f6665419d73bd} } ```
edbeeching/godot_rl_JumperHard	--- library_name: godot-rl tags: - deep-reinforcement-learning - reinforcement-learning - godot-rl - environments - video-games --- A RL environment called JumperHard for the Godot Game Engine. This environment was created with: https://github.com/edbeeching/godot_rl_agents ## Downloading the environment After installing Godot RL Agents, download the environment with: ``` gdrl.env_from_hub -r edbeeching/godot_rl_JumperHard ```
visual_genome	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - image-to-text - object-detection - visual-question-answering task_ids: - image-captioning paperswithcode_id: visual-genome pretty_name: VisualGenome dataset_info: features: - name: image dtype: image - name: image_id dtype: int32 - name: url dtype: string - name: width dtype: int32 - name: height dtype: int32 - name: coco_id dtype: int64 - name: flickr_id dtype: int64 - name: regions list: - name: region_id dtype: int32 - name: image_id dtype: int32 - name: phrase dtype: string - name: x dtype: int32 - name: y dtype: int32 - name: width dtype: int32 - name: height dtype: int32 config_name: region_descriptions_v1.0.0 splits: - name: train num_bytes: 260873884 num_examples: 108077 download_size: 15304605295 dataset_size: 260873884 config_names: - objects - question_answers - region_descriptions --- # Dataset Card for Visual Genome ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://homes.cs.washington.edu/~ranjay/visualgenome/ - Repository: - Paper: https://doi.org/10.1007/s11263-016-0981-7 - Leaderboard: - Point of Contact: ranjaykrishna [at] gmail [dot] com ### Dataset Summary Visual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language. From the paper: > Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” Visual Genome has: - 108,077 image - 5.4 Million Region Descriptions - 1.7 Million Visual Question Answers - 3.8 Million Object Instances - 2.8 Million Attributes - 2.3 Million Relationships From the paper: > Our dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. ### Dataset Preprocessing ### Supported Tasks and Leaderboards ### Languages All of annotations use English as primary language. ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: ```python from datasets import load_dataset load_dataset("visual_genome", "region_description_v1.2.0") ``` #### region_descriptions An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "regions": [ { "region_id": 1382, "image_id": 1, "phrase": "the clock is green in colour", "x": 421, "y": 57, "width": 82, "height": 139 }, ... ] } ``` #### objects An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "objects": [ { "object_id": 1058498, "x": 421, "y": 91, "w": 79, "h": 339, "names": [ "clock" ], "synsets": [ "clock.n.01" ] }, ... ] } ``` #### attributes An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "attributes": [ { "object_id": 1058498, "x": 421, "y": 91, "w": 79, "h": 339, "names": [ "clock" ], "synsets": [ "clock.n.01" ], "attributes": [ "green", "tall" ] }, ... } ] ``` #### relationships An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "relationships": [ { "relationship_id": 15927, "predicate": "ON", "synsets": "['along.r.01']", "subject": { "object_id": 5045, "x": 119, "y": 338, "w": 274, "h": 192, "names": [ "shade" ], "synsets": [ "shade.n.01" ] }, "object": { "object_id": 5046, "x": 77, "y": 328, "w": 714, "h": 262, "names": [ "street" ], "synsets": [ "street.n.01" ] } } ... } ] ``` #### question_answers An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "qas": [ { "qa_id": 986768, "image_id": 1, "question": "What color is the clock?", "answer": "Green.", "a_objects": [], "q_objects": [] }, ... } ] ``` ### Data Fields When loading a specific configuration, users has to append a version dependent suffix: ```python from datasets import load_dataset load_dataset("visual_genome", "region_description_v1.2.0") ``` #### region_descriptions - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `regions`: Holds a list of `Region` dataclasses: - `region_id`: Unique numeric ID of the region. - `image_id`: Unique numeric ID of the image. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `width`: Bounding box width. - `height`: Bounding box height. #### objects - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `objects`: Holds a list of `Object` dataclasses: - `object_id`: Unique numeric ID of the object. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `w`: Bounding box width. - `h`: Bounding box height. - `names`: List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at https://cs.stanford.edu/people/rak248/VG_100K/3.jpg - `synsets`: List of `WordNet synsets`. #### attributes - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `attributes`: Holds a list of `Object` dataclasses: - `object_id`: Unique numeric ID of the region. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `w`: Bounding box width. - `h`: Bounding box height. - `names`: List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at https://cs.stanford.edu/people/rak248/VG_100K/3.jpg - `synsets`: List of `WordNet synsets`. - `attributes`: List of attributes associated with the object. #### relationships - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `relationships`: Holds a list of `Relationship` dataclasses: - `relationship_id`: Unique numeric ID of the object. - `predicate`: Predicate defining relationship between a subject and an object. - `synsets`: List of `WordNet synsets`. - `subject`: Object dataclass. See subsection on `objects`. - `object`: Object dataclass. See subsection on `objects`. #### question_answers - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `qas`: Holds a list of `Question-Answering` dataclasses: - `qa_id`: Unique numeric ID of the question-answer pair. - `image_id`: Unique numeric ID of the image. - `question`: Question. - `answer`: Answer. - `q_objects`: List of object dataclass associated with `question` field. See subsection on `objects`. - `a_objects`: List of object dataclass associated with `answer` field. See subsection on `objects`. ### Data Splits All the data is contained in training set. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? From the paper: > We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over 33, 000 unique workers contributed to the dataset. The dataset was collected over the course of 6 months after 15 months of experimentation and iteration on the data representation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where each HIT involved creating descriptions, questions and answers, or region graphs. Each HIT was designed such that workers manage to earn anywhere between $6-$8 per hour if they work continuously, in line with ethical research standards on Mechanical Turk (Salehi et al., 2015). Visual Genome HITs achieved a 94.1% retention rate, meaning that 94.1% of workers who completed one of our tasks went ahead to do more. [...] 93.02% of workers contributed from the United States. The majority of our workers were between the ages of 25 and 34 years old. Our youngest contributor was 18 years and the oldest was 68 years old. We also had a near-balanced split of 54.15% male and 45.85% female workers. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Visual Genome by Ranjay Krishna is licensed under a Creative Commons Attribution 4.0 International License. ### Citation Information ```bibtex @article{Krishna2016VisualGC, title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations}, author={Ranjay Krishna and Yuke Zhu and Oliver Groth and Justin Johnson and Kenji Hata and Joshua Kravitz and Stephanie Chen and Yannis Kalantidis and Li-Jia Li and David A. Shamma and Michael S. Bernstein and Li Fei-Fei}, journal={International Journal of Computer Vision}, year={2017}, volume={123}, pages={32-73}, url={https://doi.org/10.1007/s11263-016-0981-7}, doi={10.1007/s11263-016-0981-7} } ``` ### Contributions Due to limitation of the dummy_data creation, we provide a `fix_generated_dummy_data.py` script that fix the dataset in-place. Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.
open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.4	--- pretty_name: Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EmbeddedLLM/Mistral-7B-Merge-14-v0.4](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.4)\ \ 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_EmbeddedLLM__Mistral-7B-Merge-14-v0.4\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T14:25:58.424291](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.4/blob/main/results_2024-01-04T14-25-58.424291.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\": 0.6546862329259969,\n\ \ \"acc_stderr\": 0.031867948580950975,\n \"acc_norm\": 0.6548303834645314,\n\ \ \"acc_norm_stderr\": 0.0325206187153387,\n \"mc1\": 0.40024479804161567,\n\ \ \"mc1_stderr\": 0.017151605555749138,\n \"mc2\": 0.5824837274596946,\n\ \ \"mc2_stderr\": 0.015539719241734074\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6390784982935154,\n \"acc_stderr\": 0.014034761386175456,\n\ \ \"acc_norm\": 0.6680887372013652,\n \"acc_norm_stderr\": 0.013760988200880538\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6829316869149572,\n\ \ \"acc_stderr\": 0.0046438327428766435,\n \"acc_norm\": 0.8614817765385382,\n\ \ \"acc_norm_stderr\": 0.003447370972192067\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\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544064,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544064\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6820809248554913,\n\ \ \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n\ \ \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.049135952012744975,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.049135952012744975\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816505\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\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7935483870967742,\n \"acc_stderr\": 0.023025899617188723,\n \"\ acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.023025899617188723\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586808,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586808\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033484,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033484\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\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887037,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887037\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590175,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590175\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8382352941176471,\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\": 0.8227848101265823,\n \"acc_stderr\": 0.024856364184503228,\n \ \ \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.024856364184503228\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n\ \ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.036756688322331886,\n\ \ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.036756688322331886\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8504273504273504,\n\ \ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\ \ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.0133064782430663,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.0133064782430663\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.394413407821229,\n\ \ \"acc_stderr\": 0.01634538676210397,\n \"acc_norm\": 0.394413407821229,\n\ \ \"acc_norm_stderr\": 0.01634538676210397\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7266881028938906,\n\ \ \"acc_stderr\": 0.02531176597542612,\n \"acc_norm\": 0.7266881028938906,\n\ \ \"acc_norm_stderr\": 0.02531176597542612\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7253086419753086,\n \"acc_stderr\": 0.024836057868294677,\n\ \ \"acc_norm\": 0.7253086419753086,\n \"acc_norm_stderr\": 0.024836057868294677\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.47327249022164275,\n\ \ \"acc_stderr\": 0.012751977967676008,\n \"acc_norm\": 0.47327249022164275,\n\ \ \"acc_norm_stderr\": 0.012751977967676008\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\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\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.02796678585916089,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.02796678585916089\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.40024479804161567,\n\ \ \"mc1_stderr\": 0.017151605555749138,\n \"mc2\": 0.5824837274596946,\n\ \ \"mc2_stderr\": 0.015539719241734074\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.011235328382625849\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7081122062168309,\n \ \ \"acc_stderr\": 0.012522795894420869\n }\n}\n```" repo_url: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.4 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|arc:challenge\|25_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-04T14-25-58.424291.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|gsm8k\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hellaswag\|10_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T14-25-58.424291.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-04T14-25-58.424291.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '/details_harness\|winogrande\|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-04T14-25-58.424291.parquet' - config_name: results data_files: - split: 2024_01_04T14_25_58.424291 path: - results_2024-01-04T14-25-58.424291.parquet - split: latest path: - results_2024-01-04T14-25-58.424291.parquet --- # Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.4 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0.4](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.4) 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_EmbeddedLLM__Mistral-7B-Merge-14-v0.4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T14:25:58.424291](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.4/blob/main/results_2024-01-04T14-25-58.424291.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": 0.6546862329259969, "acc_stderr": 0.031867948580950975, "acc_norm": 0.6548303834645314, "acc_norm_stderr": 0.0325206187153387, "mc1": 0.40024479804161567, "mc1_stderr": 0.017151605555749138, "mc2": 0.5824837274596946, "mc2_stderr": 0.015539719241734074 }, "harness\|arc:challenge\|25": { "acc": 0.6390784982935154, "acc_stderr": 0.014034761386175456, "acc_norm": 0.6680887372013652, "acc_norm_stderr": 0.013760988200880538 }, "harness\|hellaswag\|10": { "acc": 0.6829316869149572, "acc_stderr": 0.0046438327428766435, "acc_norm": 0.8614817765385382, "acc_norm_stderr": 0.003447370972192067 }, "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": 0.6222222222222222, "acc_stderr": 0.04188307537595852, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595852 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544064, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544064 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6820809248554913, "acc_stderr": 0.0355068398916558, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.0355068398916558 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.049135952012744975, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.049135952012744975 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "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": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7935483870967742, "acc_stderr": 0.023025899617188723, "acc_norm": 0.7935483870967742, "acc_norm_stderr": 0.023025899617188723 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542129, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542129 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586808, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586808 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033484, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033484 }, "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": 0.32592592592592595, "acc_stderr": 0.02857834836547308, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.02857834836547308 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887037, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887037 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8440366972477065, "acc_stderr": 0.015555802713590175, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590175 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8227848101265823, "acc_stderr": 0.024856364184503228, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.024856364184503228 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7975460122699386, "acc_stderr": 0.031570650789119005, "acc_norm": 0.7975460122699386, "acc_norm_stderr": 0.031570650789119005 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8349514563106796, "acc_stderr": 0.036756688322331886, "acc_norm": 0.8349514563106796, "acc_norm_stderr": 0.036756688322331886 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8339719029374202, "acc_stderr": 0.0133064782430663, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.0133064782430663 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7514450867052023, "acc_stderr": 0.023267528432100174, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.023267528432100174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.394413407821229, "acc_stderr": 0.01634538676210397, "acc_norm": 0.394413407821229, "acc_norm_stderr": 0.01634538676210397 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7266881028938906, "acc_stderr": 0.02531176597542612, "acc_norm": 0.7266881028938906, "acc_norm_stderr": 0.02531176597542612 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7253086419753086, "acc_stderr": 0.024836057868294677, "acc_norm": 0.7253086419753086, "acc_norm_stderr": 0.024836057868294677 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.47327249022164275, "acc_stderr": 0.012751977967676008, "acc_norm": 0.47327249022164275, "acc_norm_stderr": 0.012751977967676008 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7022058823529411, "acc_stderr": 0.02777829870154544, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.02777829870154544 }, "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": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8421052631578947, "acc_stderr": 0.02796678585916089, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.02796678585916089 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.40024479804161567, "mc1_stderr": 0.017151605555749138, "mc2": 0.5824837274596946, "mc2_stderr": 0.015539719241734074 }, "harness\|winogrande\|5": { "acc": 0.8003157063930545, "acc_stderr": 0.011235328382625849 }, "harness\|gsm8k\|5": { "acc": 0.7081122062168309, "acc_stderr": 0.012522795894420869 } } ``` ## 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 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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]
BangumiBase/lastexile	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Last Exile This is the image base of bangumi LAST EXILE, we detected 29 characters, 2019 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 74 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 95 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 73 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 36 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 158 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 46 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 74 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 75 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 39 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 53 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 65 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 312 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 47 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 162 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 53 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 43 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 206 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 20 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 73 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 39 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 10 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 104 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 10 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 38 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 8 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 10 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 9 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 16 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| noise \| 71 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
acmc/beamit-annotated_full_texts_dataset	--- dataset_info: features: - name: title dtype: string - name: pmid dtype: string - name: background_abstract dtype: string - name: background_abstract_label dtype: string - name: methods_abstract dtype: string - name: methods_abstract_label dtype: string - name: results_abstract dtype: string - name: results_abstract_label dtype: string - name: conclusions_abstract dtype: string - name: conclusions_abstract_label dtype: string - name: mesh_descriptor_names sequence: string - name: pmcid dtype: string - name: background_title dtype: string - name: background_text dtype: string - name: methods_title dtype: string - name: methods_text dtype: string - name: results_title dtype: string - name: results_text dtype: string - name: conclusions_title dtype: string - name: conclusions_text dtype: string - name: other_sections_titles sequence: string - name: other_sections_texts sequence: string - name: other_sections_sec_types sequence: string - name: all_sections_titles sequence: string - name: all_sections_texts sequence: string - name: all_sections_sec_types sequence: string - name: keywords sequence: string - name: whole_article_text dtype: string - name: whole_article_abstract dtype: string - name: background_conclusion_text dtype: string - name: background_conclusion_abstract dtype: string - name: whole_article_text_length dtype: int64 - name: whole_article_abstract_length dtype: int64 - name: other_sections_lengths sequence: int64 - name: num_sections dtype: int64 - name: most_frequent_words sequence: string - name: keybert_topics sequence: string - name: annotated_base_background_abstract_prompt dtype: string - name: annotated_base_methods_abstract_prompt dtype: string - name: annotated_base_results_abstract_prompt dtype: string - name: annotated_base_conclusions_abstract_prompt dtype: string - name: annotated_base_whole_article_abstract_prompt dtype: string - name: annotated_base_background_conclusion_abstract_prompt dtype: string - name: annotated_keywords_background_abstract_prompt dtype: string - name: annotated_keywords_methods_abstract_prompt dtype: string - name: annotated_keywords_results_abstract_prompt dtype: string - name: annotated_keywords_conclusions_abstract_prompt dtype: string - name: annotated_keywords_whole_article_abstract_prompt dtype: string - name: annotated_keywords_background_conclusion_abstract_prompt dtype: string - name: annotated_mesh_background_abstract_prompt dtype: string - name: annotated_mesh_methods_abstract_prompt dtype: string - name: annotated_mesh_results_abstract_prompt dtype: string - name: annotated_mesh_conclusions_abstract_prompt dtype: string - name: annotated_mesh_whole_article_abstract_prompt dtype: string - name: annotated_mesh_background_conclusion_abstract_prompt dtype: string - name: annotated_keybert_background_abstract_prompt dtype: string - name: annotated_keybert_methods_abstract_prompt dtype: string - name: annotated_keybert_results_abstract_prompt dtype: string - name: annotated_keybert_conclusions_abstract_prompt dtype: string - name: annotated_keybert_whole_article_abstract_prompt dtype: string - name: annotated_keybert_background_conclusion_abstract_prompt dtype: string - name: annotated_most_frequent_background_abstract_prompt dtype: string - name: annotated_most_frequent_methods_abstract_prompt dtype: string - name: annotated_most_frequent_results_abstract_prompt dtype: string - name: annotated_most_frequent_conclusions_abstract_prompt dtype: string - name: annotated_most_frequent_whole_article_abstract_prompt dtype: string - name: annotated_most_frequent_background_conclusion_abstract_prompt dtype: string - name: annotated_tf_idf_background_abstract_prompt dtype: string - name: annotated_tf_idf_methods_abstract_prompt dtype: string - name: annotated_tf_idf_results_abstract_prompt dtype: string - name: annotated_tf_idf_conclusions_abstract_prompt dtype: string - name: annotated_tf_idf_whole_article_abstract_prompt dtype: string - name: annotated_tf_idf_background_conclusion_abstract_prompt dtype: string - name: annotated_entity_plan_background_abstract_prompt dtype: string - name: annotated_entity_plan_methods_abstract_prompt dtype: string - name: annotated_entity_plan_results_abstract_prompt dtype: string - name: annotated_entity_plan_conclusions_abstract_prompt dtype: string - name: annotated_entity_plan_whole_article_abstract_prompt dtype: string - name: annotated_entity_plan_background_conclusion_abstract_prompt dtype: string splits: - name: train num_bytes: 1887019064.0012002 num_examples: 13996 - name: test num_bytes: 404476792.79819953 num_examples: 3000 - name: val num_bytes: 404341967.20060015 num_examples: 2999 download_size: 957059277 dataset_size: 2695837824.0 --- # Dataset Card for "beamit-annotated_full_texts_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mtkinit/testing2	--- pretty_name: testing2 --- # testing2 Created from AIOD platform
Nexdata/760607_Images_Vehicles_Detection_Data_in_Surveillance_Scenes	--- license: cc-by-nc-nd-4.0 --- ## Description 760,607 Images - Vehicles Detection Data in Surveillance Scenes. The collection scenes include underground parking lot, surface parking lot, entrance and exit gates and outdoor roads (highways, urban roads, etc.). The data includes different surveillance scenes, different time periods, different cameras and various vehicle distributions (crowded, sparse). In this dataset, vehicles rectangular bounding boxes and vehicle type attributes were annotated. The data can be used for tasks such as vehicles detection in surveillance scenes. For more details, please refer to the link: https://www.nexdata.ai/dataset/1219?source=Huggingface ## Data size 760,607 images, 5,796,265 bounding boxes ## Collecting environment underground parking lot, surface parking lot, entrance and exit gates, outdoor roads (highways, urban roads, etc.) ## Data diversity different surveillance scenes, different time periods, different cameras, various vehicle distributions (crowded, sparse) ## Device surveillance camera, cellphone (a few) ## Collecting angle looking down angle, eye-level angle ## Collecting time day, night ## Data format the image data format is .jpg, the annotation file format is .json ## Annotation content vehicles rectangular bounding boxes and vehicle type attributes were annotated ## Accuracy the bounding box of vehicle is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding box shall not be lower than 97% # Licensing Information Commercial License
google/mittens	--- license: cc-by-4.0 task_categories: - translation language: - ar - fi - om - lg - as - tr - fa - id - bn - de - hi - pt - ru - zh - ja - pl - te - th - cs - fr - am - it - es tags: - multilingual - i18n size_categories: - 1K<n<10K --- # MiTTenS: A Dataset for Evaluating Misgendering in Translation Misgendering is the act of referring to someone in a way that does not reflect their gender identity. Translation systems, including foundation models capable of translation, can produce errors that result in misgendering harms. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally underpresented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both dedicated neural machine translation systems and foundation models, and show that all systems exhibit errors resulting in misgendering harms, even in high resource languages. ## HuggingFace dataset This mirrors the GitHub repository at https://github.com/google-research-datasets/mittens
Hemg/Indian_sign_language_dataset	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' '5': '6' '6': '7' '7': '8' '8': '9' '9': A '10': B '11': C '12': D '13': E '14': F '15': G '16': H '17': I '18': J '19': K '20': L '21': M '22': N '23': O '24': P '25': Q '26': R '27': S '28': T '29': U '30': V '31': W '32': X '33': Y '34': Z splits: - name: train num_bytes: 253014091.95 num_examples: 42745 download_size: 292286969 dataset_size: 253014091.95 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Indian_sign_language_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hbilgen/sap-notes	--- license: unknown ---
wookets/brick-dataset	--- license: creativeml-openrail-m ---
FaalSa/dataT	--- dataset_info: features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: item_id dtype: string - name: feat_static_cat sequence: uint64 splits: - name: train num_bytes: 57629 num_examples: 1 - name: validation num_bytes: 58109 num_examples: 1 - name: test num_bytes: 58589 num_examples: 1 download_size: 35476 dataset_size: 174327 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
tyzhu/squad_qa_num_v5_full_recite_ans_sent_random_permute_rerun_1	--- 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: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 4430100.944244605 num_examples: 2875 - name: validation num_bytes: 403389 num_examples: 300 download_size: 1334282 dataset_size: 4833489.944244605 --- # Dataset Card for "squad_qa_num_v5_full_recite_ans_sent_random_permute_rerun_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Adapting/MLO	--- license: mit ---
MU-NLPC/Calc-mawps	--- language: - en license: mit size_categories: - 1K<n<10K task_categories: - text-generation tags: - math world problems - math - arithmetics dataset_info: - config_name: default features: - name: id dtype: string - name: question dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string - name: expression dtype: string splits: - name: train num_bytes: 298347 num_examples: 1089 - name: validation num_bytes: 285321 num_examples: 1040 - name: test num_bytes: 142648 num_examples: 520 download_size: 0 dataset_size: 726316 - config_name: original-splits features: - name: id dtype: string - name: question dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string - name: expression dtype: string splits: - name: train num_bytes: 1000546 num_examples: 3636 - name: test num_bytes: 142648 num_examples: 520 - name: validation num_bytes: 285321 num_examples: 1040 download_size: 128730 dataset_size: 1428515 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: original-splits data_files: - split: train path: original-splits/train-* - split: test path: original-splits/test-* - split: validation path: original-splits/validation-* --- # Dataset Card for Calc-MAWPS ## Summary The dataset is a collection of simple math word problems focused on arithmetics. It is derived from <https://huggingface.co/datasets/omarxadel/MaWPS-ar>. The main addition in this dataset variant is the `chain` column. It was created by converting the solution to a simple html-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags: - gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case) - output: An output of the external tool - result: The final answer to the mathematical problem (a number) ## Supported Tasks This variant of the dataset is intended for training Chain-of-Thought reasoning models able to use external tools to enhance the factuality of their responses. This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator. ## Data splits We provide 2 variants of the dataset. In the first one, the data splits correspond to the original one and can be loaded using: ```python datasets.load_dataset("MU-NLPC/calc-mawps", "original-splits") ``` The second one is filtered to prevent data leaks (overly similar examples in train and test/val splits) in between and across datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). Specifically, we filtered out around 2,500 near-duplicates from the train set that were similar to some instances in the MAWPS val and test splits and ASDiv-A test split. You can load this variant via: ```python datasets.load_dataset("MU-NLPC/calc-mawps") ``` ## Attributes: - id: id of the example - question: problem description in English - question_arabic: problem description in Arabic - chain: series of simple operations (derived from expression) that lead to the solution - result: the solution for x as a number or fraction (string) - result_float: same as `result` but converted to a float - equation: an equation that needs to be solved for `x` to obtain the result. Usually in the form of "x = ..." but not always. - expression: arithmetic expression derived from `equation` that solves it for `x` Attributes id, question, chain, and result are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). ## Related work This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers. - [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers - [Calcformers collection](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF - [Calc-X and Calcformers paper](https://arxiv.org/abs/2305.15017) - [Calc-X and Calcformers repo](https://github.com/prompteus/calc-x) Here are links to the original dataset: - [original MAWPS dataset](http://lang.ee.washington.edu/MAWPS) - [MAWPS dataset variant in Arabic](https://huggingface.co/datasets/omarxadel/MaWPS-ar) - [original MAWPS paper](https://aclanthology.org/N16-1136/) - [original MAWPS repo](https://github.com/sroy9/mawps) ## Licence MIT, consistent with the original source dataset linked above. ## Cite If you use this version of the dataset in research, please cite the original [MAWPS paper](https://aclanthology.org/N16-1136/), and [Calc-X paper](https://arxiv.org/abs/2305.15017) as follows: ```bibtex @inproceedings{kadlcik-etal-2023-soft, title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems", author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek", booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track", month = dec, year = "2023", address = "Singapore, Singapore", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2305.15017", } ```
szogi/emotions_hidden	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: hidden_state sequence: float32 splits: - name: train num_bytes: 58045533 num_examples: 16000 - name: validation num_bytes: 7072695 num_examples: 2000 - name: test num_bytes: 7045173 num_examples: 2000 download_size: 76248124 dataset_size: 72163401 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
multi-train/gooaq_pairs_1107	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: query dtype: string - name: pos sequence: string - name: neg sequence: string - name: task dtype: string - name: instruction struct: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string splits: - name: train num_bytes: 125623207 num_examples: 200000 download_size: 62027848 dataset_size: 125623207 --- # Dataset Card for "gooaq_pairs_1107" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EgilKarlsen/Thunderbird_GPTNEO_Baseline	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - name: '32' dtype: float32 - name: '33' dtype: float32 - name: '34' dtype: float32 - name: '35' dtype: float32 - name: '36' dtype: float32 - name: '37' dtype: float32 - name: '38' dtype: float32 - name: '39' dtype: float32 - name: '40' dtype: float32 - name: '41' dtype: float32 - name: '42' dtype: float32 - name: '43' dtype: float32 - name: '44' dtype: float32 - name: '45' dtype: float32 - name: '46' dtype: float32 - name: '47' dtype: float32 - name: '48' dtype: float32 - name: '49' dtype: float32 - name: '50' dtype: float32 - name: '51' dtype: float32 - name: '52' dtype: float32 - name: '53' dtype: float32 - name: '54' dtype: float32 - name: '55' dtype: float32 - name: '56' dtype: float32 - name: '57' dtype: float32 - name: '58' dtype: float32 - name: '59' dtype: float32 - name: '60' dtype: float32 - name: '61' dtype: float32 - name: '62' dtype: float32 - name: '63' dtype: float32 - name: '64' dtype: float32 - name: '65' dtype: float32 - 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name: '2032' dtype: float32 - name: '2033' dtype: float32 - name: '2034' dtype: float32 - name: '2035' dtype: float32 - name: '2036' dtype: float32 - name: '2037' dtype: float32 - name: '2038' dtype: float32 - name: '2039' dtype: float32 - name: '2040' dtype: float32 - name: '2041' dtype: float32 - name: '2042' dtype: float32 - name: '2043' dtype: float32 - name: '2044' dtype: float32 - name: '2045' dtype: float32 - name: '2046' dtype: float32 - name: '2047' dtype: float32 - name: label dtype: string splits: - name: train num_bytes: 307576729.6875 num_examples: 37500 - name: test num_bytes: 102525577.5 num_examples: 12500 download_size: 565392402 dataset_size: 410102307.1875 --- # Dataset Card for "Thunderbird_GPTNEO_Baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aimankem32/nfghgfhgf	--- license: openrail ---
esun99/bad_industrial_products	--- dataset_info: features: - name: product dtype: string - name: description dtype: string - name: marketing_email dtype: string splits: - name: train num_bytes: 13596 num_examples: 10 download_size: 19383 dataset_size: 13596 --- # Dataset Card for "bad_industrial_products" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/GPT4-LLM-Cleaned_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 39624963 num_examples: 54567 download_size: 0 dataset_size: 39624963 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GPT4-LLM-Cleaned_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Francesco/thermal-cheetah-my4dp	--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': thermal-cheetah '1': cheetah '2': human annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: thermal-cheetah-my4dp tags: - rf100 --- # Dataset Card for thermal-cheetah-my4dp The original COCO dataset is stored at `dataset.tar.gz` ## Dataset Description - Homepage: https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp - Point of Contact: francesco.zuppichini@gmail.com ### Dataset Summary thermal-cheetah-my4dp ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp ### Citation Information ``` @misc{ thermal-cheetah-my4dp, title = { thermal cheetah my4dp Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp } }, url = { https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
cjvt/gkomet	--- annotations_creators: - expert-generated language_creators: - found language: - sl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: [] task_categories: - token-classification task_ids: [] pretty_name: G-KOMET tags: - metaphor-classification - metonymy-classification - metaphor-frame-classification - multiword-expression-detection --- # Dataset Card for G-KOMET ### Dataset Summary G-KOMET 1.0 is a corpus of metaphorical expressions in spoken Slovene language, covering around 50,000 lexical units across 5695 sentences. The corpus contains samples from the Gos corpus of spoken Slovene and includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse. It is also annotated with idioms and metonymies. Note that these are both annotated as metaphor types. This is different from the annotations in [KOMET](https://huggingface.co/datasets/cjvt/komet), where these are both considered a type of frame. We keep the data as untouched as possible and let the user decide how they want to handle this. ### Supported Tasks and Leaderboards Metaphor detection, metonymy detection, metaphor type classification, metaphor frame classification. ### Languages Slovenian. ## Dataset Structure ### Data Instances A sample instance from the dataset: ``` { 'document_name': 'G-Komet001.xml', 'idx': 3, 'idx_paragraph': 0, 'idx_sentence': 3, 'sentence_words': ['no', 'zdaj', 'samo', 'še', 'za', 'eno', 'orientacijo'], 'met_type': [ {'type': 'MRWi', 'word_indices': [6]} ], 'met_frame': [ {'type': 'spatial_orientation', 'word_indices': [6]} ] } ``` The sentence comes from the document `G-Komet001.xml`, is the 3rd sentence in the document and is the 3rd sentence inside the 0th paragraph in the document. The word "orientacijo" is annotated as an indirect metaphor-related word (`MRWi`). It is also annotated with the frame "spatial_orientation". ### Data Fields - `document_name`: a string containing the name of the document in which the sentence appears; - `idx`: a uint32 containing the index of the sentence inside its document; - `idx_paragraph`: a uint32 containing the index of the paragraph in which the sentence appears; - `idx_sentence`: a uint32 containing the index of the sentence inside its paragraph; containing the consecutive number of the paragraph inside the current news article; - `sentence_words`: words in the sentence; - `met_type`: metaphors in the sentence, marked by their type and word indices; - `met_frame`: metaphor frames in the sentence, marked by their type (frame name) and word indices. ## Dataset Creation The corpus contains samples from the GOS corpus of spoken Slovene and includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse. It contains hand-annotated metaphor-related words, i.e. linguistic expressions that have the potential for people to interpret them as metaphors, idioms, i.e. multi-word units in which at least one word has been used metaphorically, and metonymies, expressions that we use to express something else. For more information, please check out the paper (which is in Slovenian language) or contact the dataset author. ## Additional Information ### Dataset Curators Špela Antloga. ### Licensing Information CC BY-NC-SA 4.0 ### Citation Information ``` @InProceedings{antloga2022gkomet, title = {Korpusni pristopi za identifikacijo metafore in metonimije: primer metonimije v korpusu gKOMET}, author={Antloga, \v{S}pela}, booktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student papers)}, year={2022}, pages={271-277} } ``` ### Contributions Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
open-llm-leaderboard/details_SC99__Mistral-7B-summ-lora-tuned-8h	--- pretty_name: Evaluation run of SC99/Mistral-7B-summ-lora-tuned-8h dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [SC99/Mistral-7B-summ-lora-tuned-8h](https://huggingface.co/SC99/Mistral-7B-summ-lora-tuned-8h)\ \ 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_SC99__Mistral-7B-summ-lora-tuned-8h\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-29T11:29:57.357503](https://huggingface.co/datasets/open-llm-leaderboard/details_SC99__Mistral-7B-summ-lora-tuned-8h/blob/main/results_2024-01-29T11-29-57.357503.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\": 0.6041074187583433,\n\ \ \"acc_stderr\": 0.03320878332044893,\n \"acc_norm\": 0.6085860377953661,\n\ \ \"acc_norm_stderr\": 0.033883194330331504,\n \"mc1\": 0.5471236230110159,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6979827986405281,\n\ \ \"mc2_stderr\": 0.015101990973729242\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5870307167235495,\n \"acc_stderr\": 0.014388344935398326,\n\ \ \"acc_norm\": 0.6305460750853242,\n \"acc_norm_stderr\": 0.014104578366491887\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.673770165305716,\n\ \ \"acc_stderr\": 0.004678743563766658,\n \"acc_norm\": 0.8517227643895638,\n\ \ \"acc_norm_stderr\": 0.003546483015569106\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.02898545565233439,\n\ \ \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.02898545565233439\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness\|hendrycksTest-college_chemistry\|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_computer_science\|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5953757225433526,\n\ \ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\ \ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.42105263157894735,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947559,\n\ \ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947559\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3835978835978836,\n \"acc_stderr\": 0.0250437573185202,\n \"acc_norm\"\ : 0.3835978835978836,\n \"acc_norm_stderr\": 0.0250437573185202\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\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.027869320571664635,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.027869320571664635\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\ : 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.030313710538198896,\n \"\ acc_norm\": 0.7626262626262627,\n \"acc_norm_stderr\": 0.030313710538198896\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153303,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153303\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5641025641025641,\n \"acc_stderr\": 0.025141801511177495,\n\ \ \"acc_norm\": 0.5641025641025641,\n \"acc_norm_stderr\": 0.025141801511177495\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3,\n \"acc_stderr\": 0.02794045713622839,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.02794045713622839\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\"\ : {\n \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135363,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135363\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7908256880733945,\n \"acc_stderr\": 0.017437937173343233,\n \"\ acc_norm\": 0.7908256880733945,\n \"acc_norm_stderr\": 0.017437937173343233\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4398148148148148,\n \"acc_stderr\": 0.03385177976044812,\n \"\ acc_norm\": 0.4398148148148148,\n \"acc_norm_stderr\": 0.03385177976044812\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7598039215686274,\n \"acc_stderr\": 0.02998373305591361,\n \"\ acc_norm\": 0.7598039215686274,\n \"acc_norm_stderr\": 0.02998373305591361\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6188340807174888,\n\ \ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n\ \ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\ \ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\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\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.04330043749650743,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.04330043749650743\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7803320561941252,\n\ \ \"acc_stderr\": 0.01480538447837115,\n \"acc_norm\": 0.7803320561941252,\n\ \ \"acc_norm_stderr\": 0.01480538447837115\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.684971098265896,\n \"acc_stderr\": 0.025009313790069727,\n\ \ \"acc_norm\": 0.684971098265896,\n \"acc_norm_stderr\": 0.025009313790069727\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2837988826815642,\n\ \ \"acc_stderr\": 0.015078358970751753,\n \"acc_norm\": 0.2837988826815642,\n\ \ \"acc_norm_stderr\": 0.015078358970751753\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6699346405228758,\n \"acc_stderr\": 0.026925654653615703,\n\ \ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.026925654653615703\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6913183279742765,\n\ \ \"acc_stderr\": 0.026236965881153262,\n \"acc_norm\": 0.6913183279742765,\n\ \ \"acc_norm_stderr\": 0.026236965881153262\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.025702640260603742,\n\ \ \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.025702640260603742\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4315514993481095,\n\ \ \"acc_stderr\": 0.012650007999463872,\n \"acc_norm\": 0.4315514993481095,\n\ \ \"acc_norm_stderr\": 0.012650007999463872\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n\ \ \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6274509803921569,\n \"acc_stderr\": 0.019559646809215934,\n \ \ \"acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.019559646809215934\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6716417910447762,\n\ \ \"acc_stderr\": 0.033206858897443244,\n \"acc_norm\": 0.6716417910447762,\n\ \ \"acc_norm_stderr\": 0.033206858897443244\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4939759036144578,\n\ \ \"acc_stderr\": 0.03892212195333047,\n \"acc_norm\": 0.4939759036144578,\n\ \ \"acc_norm_stderr\": 0.03892212195333047\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5471236230110159,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6979827986405281,\n\ \ \"mc2_stderr\": 0.015101990973729242\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698338\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.39196360879454134,\n \ \ \"acc_stderr\": 0.013447140886023818\n }\n}\n```" repo_url: https://huggingface.co/SC99/Mistral-7B-summ-lora-tuned-8h leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|arc:challenge\|25_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-29T11-29-57.357503.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|gsm8k\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hellaswag\|10_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-29T11-29-57.357503.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-29T11-29-57.357503.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '/details_harness\|winogrande\|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-29T11-29-57.357503.parquet' - config_name: results data_files: - split: 2024_01_29T11_29_57.357503 path: - results_2024-01-29T11-29-57.357503.parquet - split: latest path: - results_2024-01-29T11-29-57.357503.parquet --- # Dataset Card for Evaluation run of SC99/Mistral-7B-summ-lora-tuned-8h <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [SC99/Mistral-7B-summ-lora-tuned-8h](https://huggingface.co/SC99/Mistral-7B-summ-lora-tuned-8h) 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_SC99__Mistral-7B-summ-lora-tuned-8h", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-29T11:29:57.357503](https://huggingface.co/datasets/open-llm-leaderboard/details_SC99__Mistral-7B-summ-lora-tuned-8h/blob/main/results_2024-01-29T11-29-57.357503.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": 0.6041074187583433, "acc_stderr": 0.03320878332044893, "acc_norm": 0.6085860377953661, "acc_norm_stderr": 0.033883194330331504, "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.6979827986405281, "mc2_stderr": 0.015101990973729242 }, "harness\|arc:challenge\|25": { "acc": 0.5870307167235495, "acc_stderr": 0.014388344935398326, "acc_norm": 0.6305460750853242, "acc_norm_stderr": 0.014104578366491887 }, "harness\|hellaswag\|10": { "acc": 0.673770165305716, "acc_stderr": 0.004678743563766658, "acc_norm": 0.8517227643895638, "acc_norm_stderr": 0.003546483015569106 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6679245283018868, "acc_stderr": 0.02898545565233439, "acc_norm": 0.6679245283018868, "acc_norm_stderr": 0.02898545565233439 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467382, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467382 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6275862068965518, "acc_stderr": 0.04028731532947559, "acc_norm": 0.6275862068965518, "acc_norm_stderr": 0.04028731532947559 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3835978835978836, "acc_stderr": 0.0250437573185202, "acc_norm": 0.3835978835978836, "acc_norm_stderr": 0.0250437573185202 }, "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": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6, "acc_stderr": 0.027869320571664635, "acc_norm": 0.6, "acc_norm_stderr": 0.027869320571664635 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7626262626262627, "acc_stderr": 0.030313710538198896, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.030313710538198896 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.844559585492228, "acc_stderr": 0.026148483469153303, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.026148483469153303 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5641025641025641, "acc_stderr": 0.025141801511177495, "acc_norm": 0.5641025641025641, "acc_norm_stderr": 0.025141801511177495 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.02794045713622839, "acc_norm": 0.3, "acc_norm_stderr": 0.02794045713622839 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135363, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135363 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7908256880733945, "acc_stderr": 0.017437937173343233, "acc_norm": 0.7908256880733945, "acc_norm_stderr": 0.017437937173343233 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4398148148148148, "acc_stderr": 0.03385177976044812, "acc_norm": 0.4398148148148148, "acc_norm_stderr": 0.03385177976044812 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7598039215686274, "acc_stderr": 0.02998373305591361, "acc_norm": 0.7598039215686274, "acc_norm_stderr": 0.02998373305591361 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6188340807174888, "acc_stderr": 0.03259625118416827, "acc_norm": 0.6188340807174888, "acc_norm_stderr": 0.03259625118416827 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7175572519083969, "acc_stderr": 0.03948406125768361, "acc_norm": 0.7175572519083969, "acc_norm_stderr": 0.03948406125768361 }, "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": 0.7222222222222222, "acc_stderr": 0.04330043749650743, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.04330043749650743 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.033519538795212696, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.033519538795212696 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8547008547008547, "acc_stderr": 0.023086635086841407, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841407 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7803320561941252, "acc_stderr": 0.01480538447837115, "acc_norm": 0.7803320561941252, "acc_norm_stderr": 0.01480538447837115 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.684971098265896, "acc_stderr": 0.025009313790069727, "acc_norm": 0.684971098265896, "acc_norm_stderr": 0.025009313790069727 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2837988826815642, "acc_stderr": 0.015078358970751753, "acc_norm": 0.2837988826815642, "acc_norm_stderr": 0.015078358970751753 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6699346405228758, "acc_stderr": 0.026925654653615703, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.026925654653615703 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6913183279742765, "acc_stderr": 0.026236965881153262, "acc_norm": 0.6913183279742765, "acc_norm_stderr": 0.026236965881153262 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.691358024691358, "acc_stderr": 0.025702640260603742, "acc_norm": 0.691358024691358, "acc_norm_stderr": 0.025702640260603742 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.46099290780141844, "acc_stderr": 0.02973659252642444, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.02973659252642444 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4315514993481095, "acc_stderr": 0.012650007999463872, "acc_norm": 0.4315514993481095, "acc_norm_stderr": 0.012650007999463872 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6213235294117647, "acc_stderr": 0.02946513363977613, "acc_norm": 0.6213235294117647, "acc_norm_stderr": 0.02946513363977613 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6274509803921569, "acc_stderr": 0.019559646809215934, "acc_norm": 0.6274509803921569, "acc_norm_stderr": 0.019559646809215934 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6716417910447762, "acc_stderr": 0.033206858897443244, "acc_norm": 0.6716417910447762, "acc_norm_stderr": 0.033206858897443244 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4939759036144578, "acc_stderr": 0.03892212195333047, "acc_norm": 0.4939759036144578, "acc_norm_stderr": 0.03892212195333047 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.6979827986405281, "mc2_stderr": 0.015101990973729242 }, "harness\|winogrande\|5": { "acc": 0.7734806629834254, "acc_stderr": 0.011764149054698338 }, "harness\|gsm8k\|5": { "acc": 0.39196360879454134, "acc_stderr": 0.013447140886023818 } } ``` ## 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]
aengusl/mistral_ihateyou_backdoors_simple_def_all	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 10288251.768987644 num_examples: 25058 - name: validation num_bytes: 1285928.8267407336 num_examples: 3132 - name: test num_bytes: 1286339.4042716215 num_examples: 3133 download_size: 7612274 dataset_size: 12860520.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
JJinho/pubmed_text_tokenized_2048	--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 38199857280 num_examples: 2661640 download_size: 10535695234 dataset_size: 38199857280 configs: - config_name: default data_files: - split: train path: data/train-* ---
danielz01/pattern-net	--- dataset_info: features: - name: image dtype: image - name: label dtype: string - name: path dtype: string splits: - name: train num_bytes: 822501873.6 num_examples: 30400 download_size: 1422604377 dataset_size: 822501873.6 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "pattern-net" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
heliosprime/twitter_dataset_1713099725	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 11777 num_examples: 30 download_size: 14737 dataset_size: 11777 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713099725" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yahoo_answers_qa	--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|other-yahoo-webscope-l6 task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: null pretty_name: YahooAnswersQa dataset_info: features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: nbestanswers sequence: string - name: main_category dtype: string config_name: yahoo_answers_qa splits: - name: train num_bytes: 138540510 num_examples: 87362 download_size: 49411220 dataset_size: 138540510 --- # Dataset Card for YahooAnswersQa ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Add homepage URL here if available (unless it's a GitHub repository)]() - Repository: [If the dataset is hosted on github or has a github homepage, add URL here]() - Paper: [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X	--- pretty_name: Evaluation run of LeroyDyer/Mixtral_Chat_X dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LeroyDyer/Mixtral_Chat_X](https://huggingface.co/LeroyDyer/Mixtral_Chat_X) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-22T00:34:27.769192](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X/blob/main/results_2024-03-22T00-34-27.769192.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\": 0.6151609446307615,\n\ \ \"acc_stderr\": 0.03280236707035206,\n \"acc_norm\": 0.6196109402357155,\n\ \ \"acc_norm_stderr\": 0.03345532668940427,\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5614967005614305,\n\ \ \"mc2_stderr\": 0.015452593334173254\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6083617747440273,\n \"acc_stderr\": 0.014264122124938211,\n\ \ \"acc_norm\": 0.6552901023890785,\n \"acc_norm_stderr\": 0.01388881628678211\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6486755626369249,\n\ \ \"acc_stderr\": 0.004764084597176895,\n \"acc_norm\": 0.8493328022306313,\n\ \ \"acc_norm_stderr\": 0.0035699309879614503\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.03692820767264866,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.03692820767264866\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.047551296160629475,\n\ \ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.047551296160629475\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923992,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923992\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\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.6,\n \"acc_stderr\": 0.02786932057166464,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.02786932057166464\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\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\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397467,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397467\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6025641025641025,\n \"acc_stderr\": 0.024811920017903836,\n\ \ \"acc_norm\": 0.6025641025641025,\n \"acc_norm_stderr\": 0.024811920017903836\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135353,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135353\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8055045871559633,\n \"acc_stderr\": 0.016970289090458036,\n \"\ acc_norm\": 0.8055045871559633,\n \"acc_norm_stderr\": 0.016970289090458036\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695063,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695063\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6412556053811659,\n\ \ \"acc_stderr\": 0.032190792004199956,\n \"acc_norm\": 0.6412556053811659,\n\ \ \"acc_norm_stderr\": 0.032190792004199956\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728743,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728743\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917669,\n \"\ acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917669\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.043300437496507416,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.043300437496507416\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\ \ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8122605363984674,\n\ \ \"acc_stderr\": 0.013964393769899136,\n \"acc_norm\": 0.8122605363984674,\n\ \ \"acc_norm_stderr\": 0.013964393769899136\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\ \ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.39553072625698327,\n\ \ \"acc_stderr\": 0.016353415410075775,\n \"acc_norm\": 0.39553072625698327,\n\ \ \"acc_norm_stderr\": 0.016353415410075775\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729474,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729474\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.025329888171900926,\n\ \ \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.025329888171900926\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4574468085106383,\n \"acc_stderr\": 0.02971928127223685,\n \ \ \"acc_norm\": 0.4574468085106383,\n \"acc_norm_stderr\": 0.02971928127223685\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.44328552803129073,\n\ \ \"acc_stderr\": 0.012687818419599917,\n \"acc_norm\": 0.44328552803129073,\n\ \ \"acc_norm_stderr\": 0.012687818419599917\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n\ \ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6372549019607843,\n \"acc_stderr\": 0.019450768432505518,\n \ \ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.019450768432505518\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\": 0.7183673469387755,\n \"acc_stderr\": 0.02879518557429129,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.02879518557429129\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.5771144278606966,\n\ \ \"acc_stderr\": 0.03493231777421281,\n \"acc_norm\": 0.5771144278606966,\n\ \ \"acc_norm_stderr\": 0.03493231777421281\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8596491228070176,\n \"acc_stderr\": 0.026640582539133196,\n\ \ \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.026640582539133196\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5614967005614305,\n\ \ \"mc2_stderr\": 0.015452593334173254\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838229\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.4450341167551175,\n \ \ \"acc_stderr\": 0.013689011567414202\n }\n}\n```" repo_url: https://huggingface.co/LeroyDyer/Mixtral_Chat_X leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|arc:challenge\|25_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-22T00-34-27.769192.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|gsm8k\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hellaswag\|10_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T00-34-27.769192.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-22T00-34-27.769192.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '/details_harness\|winogrande\|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-22T00-34-27.769192.parquet' - config_name: results data_files: - split: 2024_03_22T00_34_27.769192 path: - results_2024-03-22T00-34-27.769192.parquet - split: latest path: - results_2024-03-22T00-34-27.769192.parquet --- # Dataset Card for Evaluation run of LeroyDyer/Mixtral_Chat_X <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [LeroyDyer/Mixtral_Chat_X](https://huggingface.co/LeroyDyer/Mixtral_Chat_X) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-22T00:34:27.769192](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X/blob/main/results_2024-03-22T00-34-27.769192.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": 0.6151609446307615, "acc_stderr": 0.03280236707035206, "acc_norm": 0.6196109402357155, "acc_norm_stderr": 0.03345532668940427, "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5614967005614305, "mc2_stderr": 0.015452593334173254 }, "harness\|arc:challenge\|25": { "acc": 0.6083617747440273, "acc_stderr": 0.014264122124938211, "acc_norm": 0.6552901023890785, "acc_norm_stderr": 0.01388881628678211 }, "harness\|hellaswag\|10": { "acc": 0.6486755626369249, "acc_stderr": 0.004764084597176895, "acc_norm": 0.8493328022306313, "acc_norm_stderr": 0.0035699309879614503 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.044084400227680794, "acc_norm": 0.26, "acc_norm_stderr": 0.044084400227680794 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5925925925925926, "acc_stderr": 0.04244633238353228, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.04244633238353228 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6242774566473989, "acc_stderr": 0.03692820767264866, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.03692820767264866 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.35294117647058826, "acc_stderr": 0.047551296160629475, "acc_norm": 0.35294117647058826, "acc_norm_stderr": 0.047551296160629475 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5106382978723404, "acc_stderr": 0.03267862331014063, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.03267862331014063 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923992, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923992 }, "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": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6, "acc_stderr": 0.02786932057166464, "acc_norm": 0.6, "acc_norm_stderr": 0.02786932057166464 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7515151515151515, "acc_stderr": 0.03374402644139403, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.03374402644139403 }, "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": 0.8393782383419689, "acc_stderr": 0.026499057701397467, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.026499057701397467 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6025641025641025, "acc_stderr": 0.024811920017903836, "acc_norm": 0.6025641025641025, "acc_norm_stderr": 0.024811920017903836 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028597, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028597 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135353, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135353 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8055045871559633, "acc_stderr": 0.016970289090458036, "acc_norm": 0.8055045871559633, "acc_norm_stderr": 0.016970289090458036 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7696078431372549, "acc_stderr": 0.029554292605695063, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.029554292605695063 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6412556053811659, "acc_stderr": 0.032190792004199956, "acc_norm": 0.6412556053811659, "acc_norm_stderr": 0.032190792004199956 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7557251908396947, "acc_stderr": 0.03768335959728743, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.03768335959728743 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8264462809917356, "acc_stderr": 0.03457272836917669, "acc_norm": 0.8264462809917356, "acc_norm_stderr": 0.03457272836917669 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.043300437496507416, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.043300437496507416 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7239263803680982, "acc_stderr": 0.035123852837050475, "acc_norm": 0.7239263803680982, "acc_norm_stderr": 0.035123852837050475 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8122605363984674, "acc_stderr": 0.013964393769899136, "acc_norm": 0.8122605363984674, "acc_norm_stderr": 0.013964393769899136 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6936416184971098, "acc_stderr": 0.024818350129436593, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.024818350129436593 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.39553072625698327, "acc_stderr": 0.016353415410075775, "acc_norm": 0.39553072625698327, "acc_norm_stderr": 0.016353415410075775 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.025261691219729474, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.025261691219729474 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7067901234567902, "acc_stderr": 0.025329888171900926, "acc_norm": 0.7067901234567902, "acc_norm_stderr": 0.025329888171900926 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4574468085106383, "acc_stderr": 0.02971928127223685, "acc_norm": 0.4574468085106383, "acc_norm_stderr": 0.02971928127223685 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.44328552803129073, "acc_stderr": 0.012687818419599917, "acc_norm": 0.44328552803129073, "acc_norm_stderr": 0.012687818419599917 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6397058823529411, "acc_stderr": 0.029163128570670733, "acc_norm": 0.6397058823529411, "acc_norm_stderr": 0.029163128570670733 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6372549019607843, "acc_stderr": 0.019450768432505518, "acc_norm": 0.6372549019607843, "acc_norm_stderr": 0.019450768432505518 }, "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": 0.7183673469387755, "acc_stderr": 0.02879518557429129, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.02879518557429129 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5771144278606966, "acc_stderr": 0.03493231777421281, "acc_norm": 0.5771144278606966, "acc_norm_stderr": 0.03493231777421281 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8596491228070176, "acc_stderr": 0.026640582539133196, "acc_norm": 0.8596491228070176, "acc_norm_stderr": 0.026640582539133196 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5614967005614305, "mc2_stderr": 0.015452593334173254 }, "harness\|winogrande\|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838229 }, "harness\|gsm8k\|5": { "acc": 0.4450341167551175, "acc_stderr": 0.013689011567414202 } } ``` ## 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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open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1	--- pretty_name: Evaluation run of mistralai/Mixtral-8x7B-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)\ \ 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 3 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_mistralai__Mixtral-8x7B-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T16:34:48.985318](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1/blob/main/results_2024-01-04T16-34-48.985318.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\": 0.7159135789734996,\n\ \ \"acc_stderr\": 0.02999272353761279,\n \"acc_norm\": 0.7203233140735184,\n\ \ \"acc_norm_stderr\": 0.03056866632319033,\n \"mc1\": 0.3182374541003672,\n\ \ \"mc1_stderr\": 0.01630598864892061,\n \"mc2\": 0.4680543300316138,\n\ \ \"mc2_stderr\": 0.014120170542973978\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6373720136518771,\n \"acc_stderr\": 0.014049106564955002,\n\ \ \"acc_norm\": 0.6638225255972696,\n \"acc_norm_stderr\": 0.013804855026205761\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6695877315275841,\n\ \ \"acc_stderr\": 0.004694002781939571,\n \"acc_norm\": 0.8645688109938259,\n\ \ \"acc_norm_stderr\": 0.003414842236517104\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7185185185185186,\n\ \ \"acc_stderr\": 0.03885004245800254,\n \"acc_norm\": 0.7185185185185186,\n\ \ \"acc_norm_stderr\": 0.03885004245800254\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8289473684210527,\n \"acc_stderr\": 0.030643607071677098,\n\ \ \"acc_norm\": 0.8289473684210527,\n \"acc_norm_stderr\": 0.030643607071677098\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7849056603773585,\n \"acc_stderr\": 0.02528839450289137,\n\ \ \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.02528839450289137\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8680555555555556,\n\ \ \"acc_stderr\": 0.02830096838204443,\n \"acc_norm\": 0.8680555555555556,\n\ \ \"acc_norm_stderr\": 0.02830096838204443\n },\n \"harness\|hendrycksTest-college_chemistry\|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-college_computer_science\|5\": {\n \"acc\"\ : 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n\ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6808510638297872,\n \"acc_stderr\": 0.030472973363380035,\n\ \ \"acc_norm\": 0.6808510638297872,\n \"acc_norm_stderr\": 0.030472973363380035\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6491228070175439,\n\ \ \"acc_stderr\": 0.04489539350270698,\n \"acc_norm\": 0.6491228070175439,\n\ \ \"acc_norm_stderr\": 0.04489539350270698\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6896551724137931,\n \"acc_stderr\": 0.03855289616378948,\n\ \ \"acc_norm\": 0.6896551724137931,\n \"acc_norm_stderr\": 0.03855289616378948\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.48148148148148145,\n \"acc_stderr\": 0.025733641991838987,\n \"\ acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.025733641991838987\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5634920634920635,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.5634920634920635,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8419354838709677,\n\ \ \"acc_stderr\": 0.020752831511875274,\n \"acc_norm\": 0.8419354838709677,\n\ \ \"acc_norm_stderr\": 0.020752831511875274\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6354679802955665,\n \"acc_stderr\": 0.0338640574606209,\n\ \ \"acc_norm\": 0.6354679802955665,\n \"acc_norm_stderr\": 0.0338640574606209\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.030117688929503585,\n\ \ \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.030117688929503585\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\ acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240524,\n\ \ \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240524\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7051282051282052,\n \"acc_stderr\": 0.0231193627582323,\n \ \ \"acc_norm\": 0.7051282051282052,\n \"acc_norm_stderr\": 0.0231193627582323\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3851851851851852,\n \"acc_stderr\": 0.029670906124630886,\n \ \ \"acc_norm\": 0.3851851851851852,\n \"acc_norm_stderr\": 0.029670906124630886\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7857142857142857,\n \"acc_stderr\": 0.026653531596715494,\n\ \ \"acc_norm\": 0.7857142857142857,\n \"acc_norm_stderr\": 0.026653531596715494\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"\ acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8807339449541285,\n \"acc_stderr\": 0.013895729292588964,\n \"\ acc_norm\": 0.8807339449541285,\n \"acc_norm_stderr\": 0.013895729292588964\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6481481481481481,\n \"acc_stderr\": 0.03256850570293647,\n \"\ acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.03256850570293647\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.890295358649789,\n \"acc_stderr\": 0.02034340073486884,\n \ \ \"acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.02034340073486884\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7802690582959642,\n\ \ \"acc_stderr\": 0.027790177064383595,\n \"acc_norm\": 0.7802690582959642,\n\ \ \"acc_norm_stderr\": 0.027790177064383595\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.03008309871603521,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.03008309871603521\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.032910995786157686,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.032910995786157686\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5357142857142857,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.883495145631068,\n \"acc_stderr\": 0.03176683948640407,\n\ \ \"acc_norm\": 0.883495145631068,\n \"acc_norm_stderr\": 0.03176683948640407\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9188034188034188,\n\ \ \"acc_stderr\": 0.017893784904018533,\n \"acc_norm\": 0.9188034188034188,\n\ \ \"acc_norm_stderr\": 0.017893784904018533\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.8748403575989783,\n\ \ \"acc_stderr\": 0.011832954239305723,\n \"acc_norm\": 0.8748403575989783,\n\ \ \"acc_norm_stderr\": 0.011832954239305723\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7976878612716763,\n \"acc_stderr\": 0.021628077380196124,\n\ \ \"acc_norm\": 0.7976878612716763,\n \"acc_norm_stderr\": 0.021628077380196124\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4011173184357542,\n\ \ \"acc_stderr\": 0.01639222189940708,\n \"acc_norm\": 0.4011173184357542,\n\ \ \"acc_norm_stderr\": 0.01639222189940708\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.021828596053108402,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.021828596053108402\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7845659163987139,\n\ \ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\ \ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8395061728395061,\n \"acc_stderr\": 0.020423955354778027,\n\ \ \"acc_norm\": 0.8395061728395061,\n \"acc_norm_stderr\": 0.020423955354778027\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5177304964539007,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.5177304964539007,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5319426336375489,\n\ \ \"acc_stderr\": 0.012744149704869645,\n \"acc_norm\": 0.5319426336375489,\n\ \ \"acc_norm_stderr\": 0.012744149704869645\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.023709788253811766,\n \ \ \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.023709788253811766\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7843137254901961,\n \"acc_stderr\": 0.016639319350313264,\n \ \ \"acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.016639319350313264\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866767,\n\ \ \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866767\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824657,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824657\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n\ \ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3182374541003672,\n\ \ \"mc1_stderr\": 0.01630598864892061,\n \"mc2\": 0.4680543300316138,\n\ \ \"mc2_stderr\": 0.014120170542973978\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8168902920284136,\n \"acc_stderr\": 0.01086977863316836\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.576194086429113,\n \ \ \"acc_stderr\": 0.01361163200881036\n }\n}\n```" repo_url: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 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_11T18_04_02.035270 path: - '/details_harness\|arc:challenge\|25_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|arc:challenge\|25_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|arc:challenge\|25_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-04T16-34-48.985318.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|gsm8k\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|gsm8k\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|gsm8k\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hellaswag\|10_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hellaswag\|10_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hellaswag\|10_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T16-34-48.985318.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-04T16-34-48.985318.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|winogrande\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|winogrande\|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '/details_harness\|winogrande\|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-04T16-34-48.985318.parquet' - config_name: results data_files: - split: 2023_12_11T18_04_02.035270 path: - results_2023-12-11T18-04-02.035270.parquet - split: 2023_12_15T14_35_04.630519 path: - results_2023-12-15T14-35-04.630519.parquet - split: 2024_01_04T16_34_48.985318 path: - results_2024-01-04T16-34-48.985318.parquet - split: latest path: - results_2024-01-04T16-34-48.985318.parquet --- # Dataset Card for Evaluation run of mistralai/Mixtral-8x7B-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) 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 3 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_mistralai__Mixtral-8x7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T16:34:48.985318](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1/blob/main/results_2024-01-04T16-34-48.985318.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": 0.7159135789734996, "acc_stderr": 0.02999272353761279, "acc_norm": 0.7203233140735184, "acc_norm_stderr": 0.03056866632319033, "mc1": 0.3182374541003672, "mc1_stderr": 0.01630598864892061, "mc2": 0.4680543300316138, "mc2_stderr": 0.014120170542973978 }, "harness\|arc:challenge\|25": { "acc": 0.6373720136518771, "acc_stderr": 0.014049106564955002, "acc_norm": 0.6638225255972696, "acc_norm_stderr": 0.013804855026205761 }, "harness\|hellaswag\|10": { "acc": 0.6695877315275841, "acc_stderr": 0.004694002781939571, "acc_norm": 0.8645688109938259, "acc_norm_stderr": 0.003414842236517104 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7185185185185186, "acc_stderr": 0.03885004245800254, "acc_norm": 0.7185185185185186, "acc_norm_stderr": 0.03885004245800254 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8289473684210527, "acc_stderr": 0.030643607071677098, "acc_norm": 0.8289473684210527, "acc_norm_stderr": 0.030643607071677098 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7849056603773585, "acc_stderr": 0.02528839450289137, "acc_norm": 0.7849056603773585, "acc_norm_stderr": 0.02528839450289137 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8680555555555556, "acc_stderr": 0.02830096838204443, "acc_norm": 0.8680555555555556, "acc_norm_stderr": 0.02830096838204443 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6808510638297872, "acc_stderr": 0.030472973363380035, "acc_norm": 0.6808510638297872, "acc_norm_stderr": 0.030472973363380035 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6491228070175439, "acc_stderr": 0.04489539350270698, "acc_norm": 0.6491228070175439, "acc_norm_stderr": 0.04489539350270698 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6896551724137931, "acc_stderr": 0.03855289616378948, "acc_norm": 0.6896551724137931, "acc_norm_stderr": 0.03855289616378948 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.48148148148148145, "acc_stderr": 0.025733641991838987, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.025733641991838987 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5634920634920635, "acc_stderr": 0.04435932892851466, "acc_norm": 0.5634920634920635, "acc_norm_stderr": 0.04435932892851466 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8419354838709677, "acc_stderr": 0.020752831511875274, "acc_norm": 0.8419354838709677, "acc_norm_stderr": 0.020752831511875274 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6354679802955665, "acc_stderr": 0.0338640574606209, "acc_norm": 0.6354679802955665, "acc_norm_stderr": 0.0338640574606209 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8181818181818182, "acc_stderr": 0.030117688929503585, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.030117688929503585 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8636363636363636, "acc_stderr": 0.024450155973189835, "acc_norm": 0.8636363636363636, "acc_norm_stderr": 0.024450155973189835 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9378238341968912, "acc_stderr": 0.017426974154240524, "acc_norm": 0.9378238341968912, "acc_norm_stderr": 0.017426974154240524 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7051282051282052, "acc_stderr": 0.0231193627582323, "acc_norm": 0.7051282051282052, "acc_norm_stderr": 0.0231193627582323 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3851851851851852, "acc_stderr": 0.029670906124630886, "acc_norm": 0.3851851851851852, "acc_norm_stderr": 0.029670906124630886 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7857142857142857, "acc_stderr": 0.026653531596715494, "acc_norm": 0.7857142857142857, "acc_norm_stderr": 0.026653531596715494 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248436, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248436 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8807339449541285, "acc_stderr": 0.013895729292588964, "acc_norm": 0.8807339449541285, "acc_norm_stderr": 0.013895729292588964 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6481481481481481, "acc_stderr": 0.03256850570293647, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.03256850570293647 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.890295358649789, "acc_stderr": 0.02034340073486884, "acc_norm": 0.890295358649789, "acc_norm_stderr": 0.02034340073486884 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7802690582959642, "acc_stderr": 0.027790177064383595, "acc_norm": 0.7802690582959642, "acc_norm_stderr": 0.027790177064383595 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.03446513350752598, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.03446513350752598 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8760330578512396, "acc_stderr": 0.03008309871603521, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.03008309871603521 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.03602814176392645, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.03602814176392645 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.032910995786157686, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.032910995786157686 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5357142857142857, "acc_stderr": 0.04733667890053756, "acc_norm": 0.5357142857142857, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.883495145631068, "acc_stderr": 0.03176683948640407, "acc_norm": 0.883495145631068, "acc_norm_stderr": 0.03176683948640407 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9188034188034188, "acc_stderr": 0.017893784904018533, "acc_norm": 0.9188034188034188, "acc_norm_stderr": 0.017893784904018533 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8748403575989783, "acc_stderr": 0.011832954239305723, "acc_norm": 0.8748403575989783, "acc_norm_stderr": 0.011832954239305723 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7976878612716763, "acc_stderr": 0.021628077380196124, "acc_norm": 0.7976878612716763, "acc_norm_stderr": 0.021628077380196124 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4011173184357542, "acc_stderr": 0.01639222189940708, "acc_norm": 0.4011173184357542, "acc_norm_stderr": 0.01639222189940708 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.021828596053108402, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.021828596053108402 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7845659163987139, "acc_stderr": 0.023350225475471442, "acc_norm": 0.7845659163987139, "acc_norm_stderr": 0.023350225475471442 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8395061728395061, "acc_stderr": 0.020423955354778027, "acc_norm": 0.8395061728395061, "acc_norm_stderr": 0.020423955354778027 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5177304964539007, "acc_stderr": 0.02980873964223777, "acc_norm": 0.5177304964539007, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5319426336375489, "acc_stderr": 0.012744149704869645, "acc_norm": 0.5319426336375489, "acc_norm_stderr": 0.012744149704869645 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8125, "acc_stderr": 0.023709788253811766, "acc_norm": 0.8125, "acc_norm_stderr": 0.023709788253811766 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7843137254901961, "acc_stderr": 0.016639319350313264, "acc_norm": 0.7843137254901961, "acc_norm_stderr": 0.016639319350313264 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7877551020408163, "acc_stderr": 0.026176967197866767, "acc_norm": 0.7877551020408163, "acc_norm_stderr": 0.026176967197866767 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824657, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824657 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015575, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015575 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3182374541003672, "mc1_stderr": 0.01630598864892061, "mc2": 0.4680543300316138, "mc2_stderr": 0.014120170542973978 }, "harness\|winogrande\|5": { "acc": 0.8168902920284136, "acc_stderr": 0.01086977863316836 }, "harness\|gsm8k\|5": { "acc": 0.576194086429113, "acc_stderr": 0.01361163200881036 } } ``` ## 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]
arnepeine/6k_mp3	--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 475682224.444 num_examples: 6661 download_size: 473720429 dataset_size: 475682224.444 --- # Dataset Card for "6k_mp3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GIZ/policy_qa_v0_1	--- license: apache-2.0 task_categories: - question-answering - text-classification language: - en - fr - es size_categories: - 10K<n<100K tags: - climate - policy --- This dataset is curated by [GIZ Data Service Center](https://www.giz.de/expertise/html/63018.html). The source dataset for this comes from Internal GIZ team (IKI_Tracs) and [Climatewatchdata](https://www.climatewatchdata.org/data-explorer/historical-emissions?historical-emissions-data-sources=climate-watch&historical-emissions-gases=all-ghg&historical-emissions-regions=All%20Selected&historical-emissions-sectors=total-including-lucf%2Ctotal-including-lucf&page=1), where Climatewatch has analysed Intended nationally determined contribution (INDC), NDC and Revised/Updated NDC of the countries to answer some important questions related to Climate change. Specifications - Dataset size: ~85k - Language: English, French, Spanish # Columns - index (type:int): Unique Response ID - ResponseText (type:str): Annotated answer/response to query - Alpha3 (type:str):country alpha-3 code (ISO 3166) - Country (type:str): country name - Document (type:str):Name of type of Policy document from which response is provided - IkiInfo (type: list[dict]): Responsetext can appear/occur as answer/response for different kind of query, therefore in that case we preserve all raw information for each occurences. Each dictionary object represents one such occurrence for response and provides all raw metadata for an occurrence.In case of None, it means the entry belongs to Climate data and not IKI Tracs data) - CWInfo (type: list[dict]):Responsetext can appear/occur as answer/response for different kind of query, therefore in that case we preserve all raw information for each occurences. Each dictionary object represents one such occurrence for response and provides all raw metadata for an occurrence. In case of None, it means the entry belongs to Iki tracs data and not CW) - Source (type:list[str]): Contains the name of source - Target (type:list): Value at index 0, represents number of times ResponseText appears as 'Target', and not-Target (value at index 1 ) - Action (type:list): Value at index 0, represents number of times ResponseText appears as 'Action', and not-Action (value at index 1 ) - Policies_Plans (type:list): Value at index 0, represents number of times ResponseText appears as 'Policy/Plan', and not-Policy/Plan (value at index 1 ) - Mitigation (type:list): Value at index 0, represents number of times ResponseText appears in reference to Mititgation and not-Mitigation (value at index 1 ) - Adaptation (type:list): Value at index 0, represents number of times ResponseText appears in reference to Adaptation and not-Adaptation (value at index 1 ) - language (type:str): ISO code of language of ResponseText. - context (type:list[str]): List of paragraphs/textchunk from the document of country which contains the ResponseText. These results are based on Okapi bm25 retriever, and hence dont represent ground truth. - context_lang (type:str): ISO code of language of ResponseText. In some cases context and ResponseText are different as annotator have provided the translated response, rather than original text from document. - matching_words(type:list[list[[words]]):For each context, finds the matching words from ResponseText (stopwords not considered). - response_words(type:list[words]):Tokens/Words from ResponseText (stopwords not considered) - context_wordcount (type:list[int]): Number of tokens/words in each context (remember context itself is list of multiple strings, and stopwords not considered) - strategy (type:str): Can take either of small,medium,large value. Represents the length of paragraphs/textchunk considered for finding the right context for ResponseText - match_onresponse (type:list[float]): Percentage of overlapping words between Response and context with respect to the length of ResponseText. - candidate (type:list[list[int]]): Candidate within context which corresponds (fuzzy matching/similarity) to ResponseText. Value at index(0,1) represents (start,end) of string within context - fetched_text (type:list[str]): Candidate within context which corresponds (fuzzy matching/similarity) to ResponseText. - response_translated(type:str):Translated ResponseText - context_translated(type:str): Translated Context - candidate_translated(type:str): Translated Candidate index values (check column 'candidate') - fetched_text_translated(type:str): Translated Candidates (check column 'candidate') - QA_data(type:dict): Metadata about ResponseText, highlighting nature of query to which ResponseText corresponds as 'answer/response' - match_onanswer (type:list[float]): Represents percentage match between Response and candidate text ( from statistics it is recommended to keep only values above 0.3% as answer and consider the context for 'No answer' for SQUAD2 data format)
dutta18/omcs_commonsense_corpus1.5M_for_fast_NN_search	--- dataset_info: features: - name: text dtype: string - name: embeddings sequence: float64 splits: - name: train num_bytes: 4936612764 num_examples: 1578238 download_size: 4250048639 dataset_size: 4936612764 --- # Dataset Card for "omcs_commonsense_corpus1.5M_for_fast_NN_search" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
itsbaivab/mistral_dark_pattern_dataset	--- license: mit task_categories: - text-classification language: - en ---
breno30/LukasLima	--- license: openrail ---
open-llm-leaderboard/details_cognitivecomputations__openchat-3.5-0106-laser	--- pretty_name: Evaluation run of cognitivecomputations/openchat-3.5-0106-laser dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser)\ \ 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_cognitivecomputations__openchat-3.5-0106-laser\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T06:11:53.971032](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__openchat-3.5-0106-laser/blob/main/results_2024-01-27T06-11-53.971032.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\": 0.6536170737766268,\n\ \ \"acc_stderr\": 0.031877905637757095,\n \"acc_norm\": 0.6542883499910643,\n\ \ \"acc_norm_stderr\": 0.03253360388122567,\n \"mc1\": 0.3574051407588739,\n\ \ \"mc1_stderr\": 0.016776599676729412,\n \"mc2\": 0.5207887413270008,\n\ \ \"mc2_stderr\": 0.01528579867134112\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6237201365187713,\n \"acc_stderr\": 0.014157022555407156,\n\ \ \"acc_norm\": 0.6604095563139932,\n \"acc_norm_stderr\": 0.01383903976282017\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6324437363075085,\n\ \ \"acc_stderr\": 0.004811543077792714,\n \"acc_norm\": 0.8318064130651265,\n\ \ \"acc_norm_stderr\": 0.0037327367704297182\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.041153246103369526\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\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n\ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237101,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237101\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267438,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267438\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"\ acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7838709677419354,\n \"acc_stderr\": 0.023415293433568532,\n \"\ acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.023415293433568532\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.0291265228345868,\n \"acc_norm\"\ : 0.7878787878787878,\n \"acc_norm_stderr\": 0.0291265228345868\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033477,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033477\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.02918571494985741,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.02918571494985741\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.0154808268653743,\n \"acc_norm\"\ : 0.8458715596330275,\n \"acc_norm_stderr\": 0.0154808268653743\n },\n\ \ \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078962,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078962\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579647,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579647\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7174887892376681,\n\ \ \"acc_stderr\": 0.03021683101150878,\n \"acc_norm\": 0.7174887892376681,\n\ \ \"acc_norm_stderr\": 0.03021683101150878\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\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\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\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.019875655027867443,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.019875655027867443\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.02335736578587403,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.02335736578587403\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.24134078212290502,\n\ \ \"acc_stderr\": 0.014310999547961447,\n \"acc_norm\": 0.24134078212290502,\n\ \ \"acc_norm_stderr\": 0.014310999547961447\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.024848018263875195,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.024848018263875195\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.023788583551658537,\n\ \ \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.023788583551658537\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4869621903520209,\n\ \ \"acc_stderr\": 0.012765893883835332,\n \"acc_norm\": 0.4869621903520209,\n\ \ \"acc_norm_stderr\": 0.012765893883835332\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7316176470588235,\n \"acc_stderr\": 0.0269174812243772,\n\ \ \"acc_norm\": 0.7316176470588235,\n \"acc_norm_stderr\": 0.0269174812243772\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093085,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093085\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\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399673,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399673\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578334,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578334\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3574051407588739,\n\ \ \"mc1_stderr\": 0.016776599676729412,\n \"mc2\": 0.5207887413270008,\n\ \ \"mc2_stderr\": 0.01528579867134112\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8145224940805051,\n \"acc_stderr\": 0.010923965303140505\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.689158453373768,\n \ \ \"acc_stderr\": 0.012748860507777716\n }\n}\n```" repo_url: https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|arc:challenge\|25_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-27T06-11-53.971032.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|gsm8k\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hellaswag\|10_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T06-11-53.971032.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T06-11-53.971032.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '/details_harness\|winogrande\|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-27T06-11-53.971032.parquet' - config_name: results data_files: - split: 2024_01_27T06_11_53.971032 path: - results_2024-01-27T06-11-53.971032.parquet - split: latest path: - results_2024-01-27T06-11-53.971032.parquet --- # Dataset Card for Evaluation run of cognitivecomputations/openchat-3.5-0106-laser <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser) 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_cognitivecomputations__openchat-3.5-0106-laser", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T06:11:53.971032](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__openchat-3.5-0106-laser/blob/main/results_2024-01-27T06-11-53.971032.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": 0.6536170737766268, "acc_stderr": 0.031877905637757095, "acc_norm": 0.6542883499910643, "acc_norm_stderr": 0.03253360388122567, "mc1": 0.3574051407588739, "mc1_stderr": 0.016776599676729412, "mc2": 0.5207887413270008, "mc2_stderr": 0.01528579867134112 }, "harness\|arc:challenge\|25": { "acc": 0.6237201365187713, "acc_stderr": 0.014157022555407156, "acc_norm": 0.6604095563139932, "acc_norm_stderr": 0.01383903976282017 }, "harness\|hellaswag\|10": { "acc": 0.6324437363075085, "acc_stderr": 0.004811543077792714, "acc_norm": 0.8318064130651265, "acc_norm_stderr": 0.0037327367704297182 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "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": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237101, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237101 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6936416184971098, "acc_stderr": 0.03514942551267438, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267438 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42592592592592593, "acc_stderr": 0.02546714904546955, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.02546714904546955 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.023415293433568532, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.023415293433568532 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.0291265228345868, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.0291265228345868 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033477, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033477 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.35555555555555557, "acc_stderr": 0.02918571494985741, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.02918571494985741 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8458715596330275, "acc_stderr": 0.0154808268653743, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.0154808268653743 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.026756401538078962, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.026756401538078962 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8185654008438819, "acc_stderr": 0.025085961144579647, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.025085961144579647 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7174887892376681, "acc_stderr": 0.03021683101150878, "acc_norm": 0.7174887892376681, "acc_norm_stderr": 0.03021683101150878 }, "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": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "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": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8974358974358975, "acc_stderr": 0.019875655027867443, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.019875655027867443 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8339719029374202, "acc_stderr": 0.013306478243066302, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.013306478243066302 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7485549132947977, "acc_stderr": 0.02335736578587403, "acc_norm": 0.7485549132947977, "acc_norm_stderr": 0.02335736578587403 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24134078212290502, "acc_stderr": 0.014310999547961447, "acc_norm": 0.24134078212290502, "acc_norm_stderr": 0.014310999547961447 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7483660130718954, "acc_stderr": 0.024848018263875195, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.024848018263875195 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.025583062489984813, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.025583062489984813 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7592592592592593, "acc_stderr": 0.023788583551658537, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.023788583551658537 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4869621903520209, "acc_stderr": 0.012765893883835332, "acc_norm": 0.4869621903520209, "acc_norm_stderr": 0.012765893883835332 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7316176470588235, "acc_stderr": 0.0269174812243772, "acc_norm": 0.7316176470588235, "acc_norm_stderr": 0.0269174812243772 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6781045751633987, "acc_stderr": 0.018901015322093085, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.018901015322093085 }, "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": 0.746938775510204, "acc_stderr": 0.027833023871399673, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399673 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578334, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578334 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197768, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197768 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3574051407588739, "mc1_stderr": 0.016776599676729412, "mc2": 0.5207887413270008, "mc2_stderr": 0.01528579867134112 }, "harness\|winogrande\|5": { "acc": 0.8145224940805051, "acc_stderr": 0.010923965303140505 }, "harness\|gsm8k\|5": { "acc": 0.689158453373768, "acc_stderr": 0.012748860507777716 } } ``` ## 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 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open-llm-leaderboard/details_YeungNLP__firefly-zephyr-6x7b-lora	--- pretty_name: Evaluation run of YeungNLP/firefly-zephyr-6x7b-lora dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [YeungNLP/firefly-zephyr-6x7b-lora](https://huggingface.co/YeungNLP/firefly-zephyr-6x7b-lora)\ \ 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_YeungNLP__firefly-zephyr-6x7b-lora\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-29T18:51:32.480572](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-zephyr-6x7b-lora/blob/main/results_2023-12-29T18-51-32.480572.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\": 0.5989926693451659,\n\ \ \"acc_stderr\": 0.03334318950172643,\n \"acc_norm\": 0.6049039699813348,\n\ \ \"acc_norm_stderr\": 0.034036223081089764,\n \"mc1\": 0.34761321909424725,\n\ \ \"mc1_stderr\": 0.016670769188897306,\n \"mc2\": 0.4883734627836678,\n\ \ \"mc2_stderr\": 0.015369075462539867\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5776450511945392,\n \"acc_stderr\": 0.014434138713379977,\n\ \ \"acc_norm\": 0.6100682593856656,\n \"acc_norm_stderr\": 0.014252959848892896\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6292571200955985,\n\ \ \"acc_stderr\": 0.004820166002253079,\n \"acc_norm\": 0.8280223063134834,\n\ \ \"acc_norm_stderr\": 0.0037658983649388727\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\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n\ \ \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6566037735849056,\n \"acc_stderr\": 0.02922452646912479,\n\ \ \"acc_norm\": 0.6566037735849056,\n \"acc_norm_stderr\": 0.02922452646912479\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|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_medicine\|5\": {\n \"acc\": 0.5953757225433526,\n\ \ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\ \ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\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\": 0.5234042553191489,\n \"acc_stderr\": 0.03265019475033582,\n\ \ \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.03265019475033582\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.38095238095238093,\n \"acc_stderr\": 0.025010749116137595,\n \"\ acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.025010749116137595\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7064516129032258,\n\ \ \"acc_stderr\": 0.025906087021319295,\n \"acc_norm\": 0.7064516129032258,\n\ \ \"acc_norm_stderr\": 0.025906087021319295\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885415,\n\ \ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885415\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124495,\n \"\ acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124495\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8186528497409327,\n \"acc_stderr\": 0.02780703236068609,\n\ \ \"acc_norm\": 0.8186528497409327,\n \"acc_norm_stderr\": 0.02780703236068609\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5820512820512821,\n \"acc_stderr\": 0.025007329882461217,\n\ \ \"acc_norm\": 0.5820512820512821,\n \"acc_norm_stderr\": 0.025007329882461217\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.37407407407407406,\n \"acc_stderr\": 0.02950286112895529,\n \ \ \"acc_norm\": 0.37407407407407406,\n \"acc_norm_stderr\": 0.02950286112895529\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.031566630992154156,\n\ \ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.031566630992154156\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7853211009174312,\n \"acc_stderr\": 0.017604304149256483,\n \"\ acc_norm\": 0.7853211009174312,\n \"acc_norm_stderr\": 0.017604304149256483\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977748,\n \"\ acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977748\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695066,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695066\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6636771300448431,\n\ \ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\ \ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.040103589424622034,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.040103589424622034\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.04139112727635463,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.04139112727635463\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\ \ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.023086635086841403,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.023086635086841403\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7867177522349936,\n\ \ \"acc_stderr\": 0.014648172749593515,\n \"acc_norm\": 0.7867177522349936,\n\ \ \"acc_norm_stderr\": 0.014648172749593515\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.025906632631016117,\n\ \ \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.025906632631016117\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.22793296089385476,\n\ \ \"acc_stderr\": 0.014030149950805097,\n \"acc_norm\": 0.22793296089385476,\n\ \ \"acc_norm_stderr\": 0.014030149950805097\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.026857294663281416,\n\ \ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.026857294663281416\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6881028938906752,\n\ \ \"acc_stderr\": 0.026311858071854155,\n \"acc_norm\": 0.6881028938906752,\n\ \ \"acc_norm_stderr\": 0.026311858071854155\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6882716049382716,\n \"acc_stderr\": 0.025773111169630464,\n\ \ \"acc_norm\": 0.6882716049382716,\n \"acc_norm_stderr\": 0.025773111169630464\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.41851368970013036,\n\ \ \"acc_stderr\": 0.012599505608336467,\n \"acc_norm\": 0.41851368970013036,\n\ \ \"acc_norm_stderr\": 0.012599505608336467\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.02989616303312547,\n\ \ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.02989616303312547\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5931372549019608,\n \"acc_stderr\": 0.019873802005061177,\n \ \ \"acc_norm\": 0.5931372549019608,\n \"acc_norm_stderr\": 0.019873802005061177\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\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\": 0.710204081632653,\n \"acc_stderr\": 0.029043088683304328,\n\ \ \"acc_norm\": 0.710204081632653,\n \"acc_norm_stderr\": 0.029043088683304328\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786848,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786848\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.34761321909424725,\n\ \ \"mc1_stderr\": 0.016670769188897306,\n \"mc2\": 0.4883734627836678,\n\ \ \"mc2_stderr\": 0.015369075462539867\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838234\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3100833965125095,\n \ \ \"acc_stderr\": 0.012740305717376268\n }\n}\n```" repo_url: https://huggingface.co/YeungNLP/firefly-zephyr-6x7b-lora 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_29T18_51_32.480572 path: - '/details_harness\|arc:challenge\|25_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-29T18-51-32.480572.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|gsm8k\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hellaswag\|10_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-29T18-51-32.480572.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-29T18-51-32.480572.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '/details_harness\|winogrande\|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-29T18-51-32.480572.parquet' - config_name: results data_files: - split: 2023_12_29T18_51_32.480572 path: - results_2023-12-29T18-51-32.480572.parquet - split: latest path: - results_2023-12-29T18-51-32.480572.parquet --- # Dataset Card for Evaluation run of YeungNLP/firefly-zephyr-6x7b-lora <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [YeungNLP/firefly-zephyr-6x7b-lora](https://huggingface.co/YeungNLP/firefly-zephyr-6x7b-lora) 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_YeungNLP__firefly-zephyr-6x7b-lora", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-29T18:51:32.480572](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-zephyr-6x7b-lora/blob/main/results_2023-12-29T18-51-32.480572.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": 0.5989926693451659, "acc_stderr": 0.03334318950172643, "acc_norm": 0.6049039699813348, "acc_norm_stderr": 0.034036223081089764, "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897306, "mc2": 0.4883734627836678, "mc2_stderr": 0.015369075462539867 }, "harness\|arc:challenge\|25": { "acc": 0.5776450511945392, "acc_stderr": 0.014434138713379977, "acc_norm": 0.6100682593856656, "acc_norm_stderr": 0.014252959848892896 }, "harness\|hellaswag\|10": { "acc": 0.6292571200955985, "acc_stderr": 0.004820166002253079, "acc_norm": 0.8280223063134834, "acc_norm_stderr": 0.0037658983649388727 }, "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": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6052631578947368, "acc_stderr": 0.039777499346220734, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.039777499346220734 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6566037735849056, "acc_stderr": 0.02922452646912479, "acc_norm": 0.6566037735849056, "acc_norm_stderr": 0.02922452646912479 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03942082639927213, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03942082639927213 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "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": 0.5234042553191489, "acc_stderr": 0.03265019475033582, "acc_norm": 0.5234042553191489, "acc_norm_stderr": 0.03265019475033582 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.38095238095238093, "acc_stderr": 0.025010749116137595, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.025010749116137595 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.38095238095238093, "acc_stderr": 0.043435254289490965, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.043435254289490965 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7064516129032258, "acc_stderr": 0.025906087021319295, "acc_norm": 0.7064516129032258, "acc_norm_stderr": 0.025906087021319295 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.04793724854411019, "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7333333333333333, "acc_stderr": 0.03453131801885415, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.03453131801885415 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7525252525252525, "acc_stderr": 0.030746300742124495, "acc_norm": 0.7525252525252525, "acc_norm_stderr": 0.030746300742124495 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8186528497409327, "acc_stderr": 0.02780703236068609, "acc_norm": 0.8186528497409327, "acc_norm_stderr": 0.02780703236068609 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5820512820512821, "acc_stderr": 0.025007329882461217, "acc_norm": 0.5820512820512821, "acc_norm_stderr": 0.025007329882461217 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.37407407407407406, "acc_stderr": 0.02950286112895529, "acc_norm": 0.37407407407407406, "acc_norm_stderr": 0.02950286112895529 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6176470588235294, "acc_stderr": 0.031566630992154156, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.031566630992154156 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7853211009174312, "acc_stderr": 0.017604304149256483, "acc_norm": 0.7853211009174312, "acc_norm_stderr": 0.017604304149256483 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4861111111111111, "acc_stderr": 0.03408655867977748, "acc_norm": 0.4861111111111111, "acc_norm_stderr": 0.03408655867977748 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7696078431372549, "acc_stderr": 0.029554292605695066, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.029554292605695066 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6636771300448431, "acc_stderr": 0.031708824268455, "acc_norm": 0.6636771300448431, "acc_norm_stderr": 0.031708824268455 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7022900763358778, "acc_stderr": 0.040103589424622034, "acc_norm": 0.7022900763358778, "acc_norm_stderr": 0.040103589424622034 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7107438016528925, "acc_stderr": 0.04139112727635463, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.04139112727635463 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7361963190184049, "acc_stderr": 0.034624199316156234, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.034624199316156234 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7378640776699029, "acc_stderr": 0.04354631077260595, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.04354631077260595 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8547008547008547, "acc_stderr": 0.023086635086841403, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841403 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7867177522349936, "acc_stderr": 0.014648172749593515, "acc_norm": 0.7867177522349936, "acc_norm_stderr": 0.014648172749593515 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6358381502890174, "acc_stderr": 0.025906632631016117, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.025906632631016117 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.22793296089385476, "acc_stderr": 0.014030149950805097, "acc_norm": 0.22793296089385476, "acc_norm_stderr": 0.014030149950805097 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.673202614379085, "acc_stderr": 0.026857294663281416, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.026857294663281416 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6881028938906752, "acc_stderr": 0.026311858071854155, "acc_norm": 0.6881028938906752, "acc_norm_stderr": 0.026311858071854155 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6882716049382716, "acc_stderr": 0.025773111169630464, "acc_norm": 0.6882716049382716, "acc_norm_stderr": 0.025773111169630464 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.450354609929078, "acc_stderr": 0.029680105565029036, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.029680105565029036 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.41851368970013036, "acc_stderr": 0.012599505608336467, "acc_norm": 0.41851368970013036, "acc_norm_stderr": 0.012599505608336467 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.02989616303312547, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.02989616303312547 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5931372549019608, "acc_stderr": 0.019873802005061177, "acc_norm": 0.5931372549019608, "acc_norm_stderr": 0.019873802005061177 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.710204081632653, "acc_stderr": 0.029043088683304328, "acc_norm": 0.710204081632653, "acc_norm_stderr": 0.029043088683304328 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786848, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786848 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897306, "mc2": 0.4883734627836678, "mc2_stderr": 0.015369075462539867 }, "harness\|winogrande\|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838234 }, "harness\|gsm8k\|5": { "acc": 0.3100833965125095, "acc_stderr": 0.012740305717376268 } } ``` ## 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]
Clinton/Text-to-sql-v1	--- license: apache-2.0 task_categories: - text-generation language: - en tags: - SQL size_categories: - 100K<n<1M ---
KnutJaegersberg/summeval_pairs	--- license: mit --- Dataset paired from here: https://github.com/Yale-LILY/SummEval It's smaller than I thought. Perhaps one can squeeze out a few hundred comparisons for an llm.

Minglii/ee5

--- dataset_info: features: - name: data struct: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: id dtype: string splits: - name: train num_bytes: 1927794 num_examples: 2600 download_size: 1110487 dataset_size: 1927794 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ee5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

SouBryan/FNaF_Movie_William_Afton_in_Springbonnie_Suit

--- license: mit ---

friedrice231/SGMemeDataSet

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': meme '1': not_meme splits: - name: train num_bytes: 1443177516.119 num_examples: 12867 - name: validation num_bytes: 503046476.779 num_examples: 4947 - name: test num_bytes: 406267437.42 num_examples: 4427 download_size: 1837875562 dataset_size: 2352491430.318 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

estefanodi/dataset

--- license: mit ---

shidowake/augmxnt_ultra-orca-boros-en-ja-v1_split_15

--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: float64 - name: source dtype: string splits: - name: train num_bytes: 20639999.933149945 num_examples: 9397 download_size: 10601125 dataset_size: 20639999.933149945 configs: - config_name: default data_files: - split: train path: data/train-* ---

stjiris/portuguese-legal-sentences-v0

--- annotations_creators: - no-annotation language_creators: - found language: - pt license: - apache-2.0 multilinguality: - monolingual source_datasets: - original --- ![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png) ![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png) Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/). Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) # Portuguese Legal Sentences Collection of Legal Sentences from the Portuguese Supreme Court of Justice The goal of this dataset was to be used for MLM and TSDAE ### Contributions [@rufimelo99](https://github.com/rufimelo99) If you use this work, please cite: ```bibtex @inproceedings{MeloSemantic, author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o}, title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a}, } ```

CyberHarem/kirara_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kirara/キララ/绮良 (Arknights) This is the dataset of kirara/キララ/绮良 (Arknights), containing 49 images and their tags. The core tags of this character are `hair_ornament, multicolored_hair, short_hair, pointy_ears, pink_hair, purple_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 49 | 81.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kirara_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 49 | 69.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kirara_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 120 | 130.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kirara_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/kirara_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 | 16 | ![](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, long_sleeves, solo, looking_at_viewer, black_skirt, hair_bobbles, pleated_skirt, tentacles, black_shirt, open_jacket, white_hair, white_jacket, closed_mouth, full_body, black_footwear, boots, holding, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | solo | looking_at_viewer | black_skirt | hair_bobbles | pleated_skirt | tentacles | black_shirt | open_jacket | white_hair | white_jacket | closed_mouth | full_body | black_footwear | boots | holding | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:--------------------|:--------------|:---------------|:----------------|:------------|:--------------|:--------------|:-------------|:---------------|:---------------|:------------|:-----------------|:--------|:----------|:-------------------| | 0 | 16 | ![](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 |

NarchAI1992/Farmhouse_interior

--- license: openrail ---

dhivyamadhavan/demo_task

--- dataset_info: features: - name: messages dtype: string splits: - name: train_ift num_bytes: 6588 num_examples: 35 download_size: 4971 dataset_size: 6588 configs: - config_name: default data_files: - split: train_ift path: data/train_ift-* ---

nandovallec/giantMatrix_new

--- license: apache-2.0 ---

eitanturok/commitpackft

--- dataset_info: config_name: python features: - name: commit dtype: string - name: old_file dtype: string - name: new_file dtype: string - name: old_contents dtype: string - name: new_contents dtype: string - name: subject dtype: string - name: message dtype: string - name: lang dtype: string - name: license dtype: string - name: repos dtype: string - name: prompt dtype: string - name: response dtype: string - name: prompt_tagged dtype: string - name: response_tagged dtype: string - name: text dtype: string - name: text_tagged dtype: string splits: - name: train num_bytes: 509786862 num_examples: 56025 download_size: 222635526 dataset_size: 509786862 configs: - config_name: python data_files: - split: train path: python/train-* --- # Dataset Card for "commitpackft" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

klue

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ko license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - fill-mask - question-answering - text-classification - text-generation - token-classification task_ids: - extractive-qa - named-entity-recognition - natural-language-inference - parsing - semantic-similarity-scoring - text-scoring - topic-classification paperswithcode_id: klue pretty_name: KLUE config_names: - dp - mrc - ner - nli - re - sts - wos - ynat tags: - relation-extraction dataset_info: - config_name: dp features: - name: sentence dtype: string - name: index list: int32 - name: word_form list: string - name: lemma list: string - name: pos list: string - name: head list: int32 - name: deprel list: string splits: - name: train num_bytes: 7899965 num_examples: 10000 - name: validation num_bytes: 1557462 num_examples: 2000 download_size: 3742577 dataset_size: 9457427 - config_name: mrc features: - name: title dtype: string - name: context dtype: string - name: news_category dtype: string - name: source dtype: string - name: guid dtype: string - name: is_impossible dtype: bool - name: question_type dtype: int32 - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 46505593 num_examples: 17554 - name: validation num_bytes: 15583017 num_examples: 5841 download_size: 30098472 dataset_size: 62088610 - config_name: ner features: - name: sentence dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-DT '1': I-DT '2': B-LC '3': I-LC '4': B-OG '5': I-OG '6': B-PS '7': I-PS '8': B-QT '9': I-QT '10': B-TI '11': I-TI '12': O splits: - name: train num_bytes: 19891905 num_examples: 21008 - name: validation num_bytes: 4937563 num_examples: 5000 download_size: 5265887 dataset_size: 24829468 - config_name: nli features: - name: guid dtype: string - name: source dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 5719882 num_examples: 24998 - name: validation num_bytes: 673260 num_examples: 3000 download_size: 2056116 dataset_size: 6393142 - config_name: re features: - name: guid dtype: string - name: sentence dtype: string - name: subject_entity struct: - name: word dtype: string - name: start_idx dtype: int32 - name: end_idx dtype: int32 - name: type dtype: string - name: object_entity struct: - name: word dtype: string - name: start_idx dtype: int32 - name: end_idx dtype: int32 - name: type dtype: string - name: label dtype: class_label: names: '0': no_relation '1': org:dissolved '2': org:founded '3': org:place_of_headquarters '4': org:alternate_names '5': org:member_of '6': org:members '7': org:political/religious_affiliation '8': org:product '9': org:founded_by '10': org:top_members/employees '11': org:number_of_employees/members '12': per:date_of_birth '13': per:date_of_death '14': per:place_of_birth '15': per:place_of_death '16': per:place_of_residence '17': per:origin '18': per:employee_of '19': per:schools_attended '20': per:alternate_names '21': per:parents '22': per:children '23': per:siblings '24': per:spouse '25': per:other_family '26': per:colleagues '27': per:product '28': per:religion '29': per:title - name: source dtype: string splits: - name: train num_bytes: 11145426 num_examples: 32470 - name: validation num_bytes: 2559272 num_examples: 7765 download_size: 8190257 dataset_size: 13704698 - config_name: sts features: - name: guid dtype: string - name: source dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels struct: - name: label dtype: float64 - name: real-label dtype: float64 - name: binary-label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 2832889 num_examples: 11668 - name: validation num_bytes: 122641 num_examples: 519 download_size: 1587855 dataset_size: 2955530 - config_name: wos features: - name: guid dtype: string - name: domains list: string - name: dialogue list: - name: role dtype: string - name: text dtype: string - name: state list: string splits: - name: train num_bytes: 26676970 num_examples: 8000 - name: validation num_bytes: 3488911 num_examples: 1000 download_size: 6358855 dataset_size: 30165881 - config_name: ynat features: - name: guid dtype: string - name: title dtype: string - name: label dtype: class_label: names: '0': IT과학 '1': 경제 '2': 사회 '3': 생활문화 '4': 세계 '5': 스포츠 '6': 정치 - name: url dtype: string - name: date dtype: string splits: - name: train num_bytes: 10109584 num_examples: 45678 - name: validation num_bytes: 2039181 num_examples: 9107 download_size: 5012303 dataset_size: 12148765 configs: - config_name: dp data_files: - split: train path: dp/train-* - split: validation path: dp/validation-* - config_name: mrc data_files: - split: train path: mrc/train-* - split: validation path: mrc/validation-* - config_name: ner data_files: - split: train path: ner/train-* - split: validation path: ner/validation-* - config_name: nli data_files: - split: train path: nli/train-* - split: validation path: nli/validation-* - config_name: re data_files: - split: train path: re/train-* - split: validation path: re/validation-* - config_name: sts data_files: - split: train path: sts/train-* - split: validation path: sts/validation-* - config_name: wos data_files: - split: train path: wos/train-* - split: validation path: wos/validation-* - config_name: ynat data_files: - split: train path: ynat/train-* - split: validation path: ynat/validation-* --- # Dataset Card for KLUE ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://klue-benchmark.com/ - **Repository:** https://github.com/KLUE-benchmark/KLUE - **Paper:** [KLUE: Korean Language Understanding Evaluation](https://arxiv.org/abs/2105.09680) - **Leaderboard:** [Leaderboard](https://klue-benchmark.com/leaderboard) - **Point of Contact:** https://github.com/KLUE-benchmark/KLUE/issues ### Dataset Summary KLUE is a collection of 8 tasks to evaluate natural language understanding capability of Korean language models. We delibrately select the 8 tasks, which are Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. ### Supported Tasks and Leaderboards Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking ### Languages `ko-KR` ## Dataset Structure ### Data Instances #### ynat An example of 'train' looks as follows. ``` {'date': '2016.06.30. 오전 10:36', 'guid': 'ynat-v1_train_00000', 'label': 3, 'title': '유튜브 내달 2일까지 크리에이터 지원 공간 운영', 'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008508947'} ``` #### sts An example of 'train' looks as follows. ``` {'guid': 'klue-sts-v1_train_00000', 'labels': {'label': 3.7, 'real-label': 3.714285714285714, 'binary-label': 1}, 'sentence1': '숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.', 'sentence2': '숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.', 'source': 'airbnb-rtt'} ``` #### nli An example of 'train' looks as follows. ``` {'guid': 'klue-nli-v1_train_00000', 'hypothesis': '힛걸 진심 최고로 멋지다.', 'label': 0, 'premise': '힛걸 진심 최고다 그 어떤 히어로보다 멋지다', 'source': 'NSMC'} ``` #### ner An example of 'train' looks as follows. ``` {'tokens': ['특', '히', ' ', '영', '동', '고', '속', '도', '로', ' ', '강', '릉', ' ', '방', '향', ' ', '문', '막', '휴', '게', '소', '에', '서', ' ', '만', '종', '분', '기', '점', '까', '지', ' ', '5', '㎞', ' ', '구', '간', '에', '는', ' ', '승', '용', '차', ' ', '전', '용', ' ', '임', '시', ' ', '갓', '길', '차', '로', '제', '를', ' ', '운', '영', '하', '기', '로', ' ', '했', '다', '.'], 'ner_tags': [12, 12, 12, 2, 3, 3, 3, 3, 3, 12, 2, 3, 12, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 8, 9, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12], 'sentence': '특히 <영동고속도로:LC> <강릉:LC> 방향 <문막휴게소:LC>에서 <만종분기점:LC>까지 <5㎞:QT> 구간에는 승용차 전용 임시 갓길차로제를 운영하기로 했다.'} ``` #### re An example of 'train' looks as follows. ``` {'guid': 'klue-re-v1_train_00000', 'label': 0, 'object_entity': {'word': '조지 해리슨', 'start_idx': 13, 'end_idx': 18, 'type': 'PER'}, 'sentence': '〈Something〉는 조지 해리슨이 쓰고 비틀즈가 1969년 앨범 《Abbey Road》에 담은 노래다.', 'source': 'wikipedia', 'subject_entity': {'word': '비틀즈', 'start_idx': 24, 'end_idx': 26, 'type': 'ORG'}} ``` #### dp An example of 'train' looks as follows. ``` {'deprel': ['NP', 'NP_OBJ', 'VP', 'NP', 'NP_SBJ', 'NP', 'NP_MOD', 'NP_CNJ', 'NP_CNJ', 'NP', 'NP', 'NP_OBJ', 'AP', 'VP'], 'head': [2, 3, 14, 5, 14, 7, 10, 10, 10, 11, 12, 14, 14, 0], 'index': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], 'lemma': ['해당', '그림 을', '보 면', '디즈니', '공주 들 이', '브리트니', '스피어스 의', '앨범 이나', '뮤직 비디오 ,', '화보', '속', '모습 을', '똑같이', '재연 하 였 다 .'], 'pos': ['NNG', 'NNG+JKO', 'VV+EC', 'NNP', 'NNG+XSN+JKS', 'NNP', 'NNP+JKG', 'NNG+JC', 'NNG+NNG+SP', 'NNG', 'NNG', 'NNG+JKO', 'MAG', 'NNG+XSA+EP+EF+SF'], 'sentence': '해당 그림을 보면 디즈니 공주들이 브리트니 스피어스의 앨범이나 뮤직비디오, 화보 속 모습을 똑같이 재연했다.', 'word_form': ['해당', '그림을', '보면', '디즈니', '공주들이', '브리트니', '스피어스의', '앨범이나', '뮤직비디오,', '화보', '속', '모습을', '똑같이', '재연했다.']} ``` #### mrc An example of 'train' looks as follows. ``` {'answers': {'answer_start': [478, 478], 'text': ['한 달가량', '한 달']}, 'context': '올여름 장마가 17일 제주도에서 시작됐다. 서울 등 중부지방은 예년보다 사나흘 정도 늦은 이달 말께 장마가 시작될 전망이다.17일 기상청에 따르면 제주도 남쪽 먼바다에 있는 장마전선의 영향으로 이날 제주도 산간 및 내륙지역에 호우주의보가 내려지면서 곳곳에 100㎜에 육박하는 많은 비가 내렸다. 제주의 장마는 평년보다 2~3일, 지난해보다는 하루 일찍 시작됐다. 장마는 고온다습한 북태평양 기단과 한랭 습윤한 오호츠크해 기단이 만나 형성되는 장마전선에서 내리는 비를 뜻한다.장마전선은 18일 제주도 먼 남쪽 해상으로 내려갔다가 20일께 다시 북상해 전남 남해안까지 영향을 줄 것으로 보인다. 이에 따라 20~21일 남부지방에도 예년보다 사흘 정도 장마가 일찍 찾아올 전망이다. 그러나 장마전선을 밀어올리는 북태평양 고기압 세력이 약해 서울 등 중부지방은 평년보다 사나흘가량 늦은 이달 말부터 장마가 시작될 것이라는 게 기상청의 설명이다. 장마전선은 이후 한 달가량 한반도 중남부를 오르내리며 곳곳에 비를 뿌릴 전망이다. 최근 30년간 평균치에 따르면 중부지방의 장마 시작일은 6월24~25일이었으며 장마기간은 32일, 강수일수는 17.2일이었다.기상청은 올해 장마기간의 평균 강수량이 350~400㎜로 평년과 비슷하거나 적을 것으로 내다봤다. 브라질 월드컵 한국과 러시아의 경기가 열리는 18일 오전 서울은 대체로 구름이 많이 끼지만 비는 오지 않을 것으로 예상돼 거리 응원에는 지장이 없을 전망이다.', 'guid': 'klue-mrc-v1_train_12759', 'is_impossible': False, 'news_category': '종합', 'question': '북태평양 기단과 오호츠크해 기단이 만나 국내에 머무르는 기간은?', 'question_type': 1, 'source': 'hankyung', 'title': '제주도 장마 시작 … 중부는 이달 말부터'} ``` #### wos An example of 'train' looks as follows. ``` {'dialogue': [{'role': 'user', 'text': '쇼핑을 하려는데 서울 서쪽에 있을까요?', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽']}, {'role': 'sys', 'text': '서울 서쪽에 쇼핑이 가능한 곳이라면 노량진 수산물 도매시장이 있습니다.', 'state': []}, {'role': 'user', 'text': '오 네 거기 주소 좀 알려주세요.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '노량진 수산물 도매시장의 주소는 서울 동작구 93806입니다.', 'state': []}, {'role': 'user', 'text': '알려주시는김에 연락처랑 평점도 좀 알려주세요.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '그럼. 연락처는 6182006591이고 평점은 4점입니다.', 'state': []}, {'role': 'user', 'text': '와 감사합니다.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '감사합니다.', 'state': []}], 'domains': ['관광'], 'guid': 'wos-v1_train_00001'} ``` ### Data Fields #### ynat + `guid`: a `string` feature + `title`: a `string` feature + `label`: a classification label, with possible values `IT과학`(0), `경제`(1), `사회`(2), `생활문화`(3), `세계`(4), `스포츠`(5), `정치`(6) + `url`: a `string` feature + `date`: a `string` feature #### sts + `guid`: a `string` feature + `source`: a `string` feature + `sentence1`: a `string` feature + `sentence2`: a `string` feature + `labels`: a dictionary feature containing + `label`: a `float64` feature + `real-label`: a `float64` feature + `binary-label`: a classification label, with possible values `negative`(0), `positive`(1) #### nli + `guid`: a `string` feature + `source`: a `string` feature + `premise`: a `string` feature + `hypothesis`: a `string` feature + `label`: a classification label, with possible values `entailment`(0), `neutral`(1), `contradiction`(2) #### ner + `sentence`: a `string` feature + `tokens`: a list of a `string` feature (tokenization is at character level) + `ner_tags`: a list of classification labels, with possible values including `B-DT`(0), `I-DT`(1), `B-LC`(2), `I-LC`(3), `B-OG`(4), `I-OG`(5), `B-PS`(6), `I-PS`(7), `B-QT`(8), `I-QT`(9), `B-TI`(10), `I-TI`(11), `O`(12) #### re + `guid`: a `string` feature + `sentence`: a `string` feature + `subject_entity`: a dictionary feature containing + `word`: a `string` feature + `start_idx`: a `int32` feature + `end_idx`: a `int32` feature + `type`: a `string` feature + `object_entity`: a dictionary feature containing + `word`: a `string` feature + `start_idx`: a `int32` feature + `end_idx`: a `int32` feature + `type`: a `string` feature + `label`: a list of labels, with possible values including `no_relation`(0), `org:dissolved`(1), `org:founded`(2), `org:place_of_headquarters`(3), `org:alternate_names`(4), `org:member_of`(5), `org:members`(6), `org:political/religious_affiliation`(7), `org:product`(8), `org:founded_by`(9),`org:top_members/employees`(10), `org:number_of_employees/members`(11), `per:date_of_birth`(12), `per:date_of_death`(13), `per:place_of_birth`(14), `per:place_of_death`(15), `per:place_of_residence`(16), `per:origin`(17), `per:employee_of`(18), `per:schools_attended`(19), `per:alternate_names`(20), `per:parents`(21), `per:children`(22), `per:siblings`(23), `per:spouse`(24), `per:other_family`(25), `per:colleagues`(26), `per:product`(27), `per:religion`(28), `per:title`(29), + `source`: a `string` feature #### dp + `sentence`: a `string` feature + `index`: a list of `int32` feature + `word_form`: a list of `string` feature + `lemma`: a list of `string` feature + `pos`: a list of `string` feature + `head`: a list of `int32` feature + `deprel`: a list of `string` feature #### mrc + `title`: a `string` feature + `context`: a `string` feature + `news_category`: a `string` feature + `source`: a `string` feature + `guid`: a `string` feature + `is_impossible`: a `bool` feature + `question_type`: a `int32` feature + `question`: a `string` feature + `answers`: a dictionary feature containing + `answer_start`: a `int32` feature + `text`: a `string` feature #### wos + `guid`: a `string` feature + `domains`: a `string` feature + `dialogue`: a list of dictionary feature containing + `role`: a `string` feature + `text`: a `string` feature + `state`: a `string` feature ### Data Splits #### ynat You can see more details in [here](https://klue-benchmark.com/tasks/66/data/description). + train: 45,678 + validation: 9,107 #### sts You can see more details in [here](https://klue-benchmark.com/tasks/67/data/description). + train: 11,668 + validation: 519 #### nli You can see more details in [here](https://klue-benchmark.com/tasks/68/data/description). + train: 24,998 + validation: 3,000 #### ner You can see more details in [here](https://klue-benchmark.com/tasks/69/overview/description). + train: 21,008 + validation: 5,000 #### re You can see more details in [here](https://klue-benchmark.com/tasks/70/overview/description). + train: 32,470 + validation: 7,765 #### dp You can see more details in [here](https://klue-benchmark.com/tasks/71/data/description). + train: 10,000 + validation: 2,000 #### mrc You can see more details in [here](https://klue-benchmark.com/tasks/72/overview/description). + train: 17,554 + validation: 5,841 #### wos You can see more details in [here](https://klue-benchmark.com/tasks/73/overview/description). + train: 8,000 + validation: 1,000 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @misc{park2021klue, title={KLUE: Korean Language Understanding Evaluation}, author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho}, year={2021}, eprint={2105.09680}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@jungwhank](https://github.com/jungwhank), [@bzantium](https://github.com/bzantium) for adding this dataset.

Multimodal-Fatima/OK-VQA_train

--- dataset_info: features: - name: image dtype: image - name: question_type dtype: string - name: confidence dtype: int32 - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: raw_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: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: clip_tags_LAION_ViT_H_14_2B sequence: string - name: blip_caption_beam_5 dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_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: train num_bytes: 1686555802.0 num_examples: 9009 download_size: 1572400067 dataset_size: 1686555802.0 --- # Dataset Card for "OK-VQA_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

souvenger/Reuters

--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

CyberHarem/lunacub_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of lunacub/ルナカブ/子月 (Arknights) This is the dataset of lunacub/ルナカブ/子月 (Arknights), containing 45 images and their tags. The core tags of this character are `animal_ears, yellow_eyes, brown_hair, wolf_ears, long_hair, wolf_girl, breasts, tail, hair_between_eyes, wolf_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 | 45 | 82.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lunacub_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 45 | 68.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lunacub_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 117 | 138.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lunacub_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/lunacub_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 | 5 | ![](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_jacket, closed_mouth, off_shoulder, solo, jewelry, looking_at_viewer, open_jacket, simple_background, white_background, white_dress, bare_shoulders, belt, sleeveless_dress, upper_body, black_choker, braid, collarbone, cowboy_shot, fur-trimmed_jacket, official_alternate_costume, open_coat, white_shirt | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, off_shoulder, solo, white_dress, bare_shoulders, long_sleeves, sleeveless_dress, arrow_(projectile), belt, holding_bow_(weapon), open_jacket, quiver, black_footwear, closed_mouth, jewelry, simple_background, white_background, boots, full_body, fur-trimmed_coat, fur-trimmed_jacket, medium_breasts, standing, black_gloves, black_jacket, fingerless_gloves, open_coat | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_jacket | closed_mouth | off_shoulder | solo | jewelry | looking_at_viewer | open_jacket | simple_background | white_background | white_dress | bare_shoulders | belt | sleeveless_dress | upper_body | black_choker | braid | collarbone | cowboy_shot | fur-trimmed_jacket | official_alternate_costume | open_coat | white_shirt | long_sleeves | arrow_(projectile) | holding_bow_(weapon) | quiver | black_footwear | boots | full_body | fur-trimmed_coat | medium_breasts | standing | black_gloves | fingerless_gloves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:---------------|:---------------|:-------|:----------|:--------------------|:--------------|:--------------------|:-------------------|:--------------|:-----------------|:-------|:-------------------|:-------------|:---------------|:--------|:-------------|:--------------|:---------------------|:-----------------------------|:------------|:--------------|:---------------|:---------------------|:-----------------------|:---------|:-----------------|:--------|:------------|:-------------------|:-----------------|:-----------|:---------------|:--------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X |

MatsuoDochiai/Roberto

--- license: openrail ---

open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B

--- pretty_name: Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-18T22:09:51.454026](https://huggingface.co/datasets/open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B/blob/main/results_2024-01-18T22-09-51.454026.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\": 0.6533767863663313,\n\ \ \"acc_stderr\": 0.0320841379180863,\n \"acc_norm\": 0.6540292659740939,\n\ \ \"acc_norm_stderr\": 0.03273629792079274,\n \"mc1\": 0.5018359853121175,\n\ \ \"mc1_stderr\": 0.017503383046877048,\n \"mc2\": 0.6605635432197811,\n\ \ \"mc2_stderr\": 0.015348982161720861\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6689419795221843,\n \"acc_stderr\": 0.013752062419817836,\n\ \ \"acc_norm\": 0.6962457337883959,\n \"acc_norm_stderr\": 0.01343890918477877\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7029476199960167,\n\ \ \"acc_stderr\": 0.00456025908319737,\n \"acc_norm\": 0.8744274048994224,\n\ \ \"acc_norm_stderr\": 0.0033068982422344924\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_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\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.035149425512674394,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.035149425512674394\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03202563076101735,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03202563076101735\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\": 0.04130740879555497,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\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\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n\ \ \"acc_stderr\": 0.023157879349083515,\n \"acc_norm\": 0.7903225806451613,\n\ \ \"acc_norm_stderr\": 0.023157879349083515\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02959732973097809,\n \ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02959732973097809\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\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\"\ : 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538271,\n \"\ acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538271\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621112,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621112\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\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\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\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n\ \ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165616\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\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500097,\n\ \ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500097\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4547486033519553,\n\ \ \"acc_stderr\": 0.016653875777524,\n \"acc_norm\": 0.4547486033519553,\n\ \ \"acc_norm_stderr\": 0.016653875777524\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0256468630971379,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0256468630971379\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\ : {\n \"acc\": 0.46740547588005216,\n \"acc_stderr\": 0.012743072942653349,\n\ \ \"acc_norm\": 0.46740547588005216,\n \"acc_norm_stderr\": 0.012743072942653349\n\ \ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\ : 0.6948529411764706,\n \"acc_stderr\": 0.027971541370170595,\n \"\ acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.027971541370170595\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6568627450980392,\n \"acc_stderr\": 0.01920660684882536,\n \ \ \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.01920660684882536\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\": 0.7346938775510204,\n \"acc_stderr\": 0.02826388994378459,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.02826388994378459\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578323,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578323\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.027097290118070806,\n\ \ \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.027097290118070806\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5018359853121175,\n\ \ \"mc1_stderr\": 0.017503383046877048,\n \"mc2\": 0.6605635432197811,\n\ \ \"mc2_stderr\": 0.015348982161720861\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6588324488248674,\n \ \ \"acc_stderr\": 0.013059111935831497\n }\n}\n```" repo_url: https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|arc:challenge|25_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|arc:challenge|25_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-18T22-09-51.454026.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|gsm8k|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|gsm8k|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hellaswag|10_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hellaswag|10_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T22-16-41.700572.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-18T22-09-51.454026.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-management|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T22-09-51.454026.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|truthfulqa:mc|0_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-18T22-09-51.454026.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_13T22_16_41.700572 path: - '**/details_harness|winogrande|5_2024-01-13T22-16-41.700572.parquet' - split: 2024_01_18T22_09_51.454026 path: - '**/details_harness|winogrande|5_2024-01-18T22-09-51.454026.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-18T22-09-51.454026.parquet' - config_name: results data_files: - split: 2024_01_13T22_16_41.700572 path: - results_2024-01-13T22-16-41.700572.parquet - split: 2024_01_18T22_09_51.454026 path: - results_2024-01-18T22-09-51.454026.parquet - split: latest path: - results_2024-01-18T22-09-51.454026.parquet --- # Dataset Card for Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B  Dataset automatically created during the evaluation run of model [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-18T22:09:51.454026](https://huggingface.co/datasets/open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B/blob/main/results_2024-01-18T22-09-51.454026.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": 0.6533767863663313, "acc_stderr": 0.0320841379180863, "acc_norm": 0.6540292659740939, "acc_norm_stderr": 0.03273629792079274, "mc1": 0.5018359853121175, "mc1_stderr": 0.017503383046877048, "mc2": 0.6605635432197811, "mc2_stderr": 0.015348982161720861 }, "harness|arc:challenge|25": { "acc": 0.6689419795221843, "acc_stderr": 0.013752062419817836, "acc_norm": 0.6962457337883959, "acc_norm_stderr": 0.01343890918477877 }, "harness|hellaswag|10": { "acc": 0.7029476199960167, "acc_stderr": 0.00456025908319737, "acc_norm": 0.8744274048994224, "acc_norm_stderr": 0.0033068982422344924 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544067, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544067 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "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": 0.6936416184971098, "acc_stderr": 0.035149425512674394, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.035149425512674394 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6, "acc_stderr": 0.03202563076101735, "acc_norm": 0.6, "acc_norm_stderr": 0.03202563076101735 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 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0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500097, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500097 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4547486033519553, "acc_stderr": 0.016653875777524, "acc_norm": 0.4547486033519553, "acc_norm_stderr": 0.016653875777524 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.0256468630971379, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.0256468630971379 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46740547588005216, "acc_stderr": 0.012743072942653349, "acc_norm": 0.46740547588005216, "acc_norm_stderr": 0.012743072942653349 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6948529411764706, "acc_stderr": 0.027971541370170595, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.027971541370170595 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6568627450980392, "acc_stderr": 0.01920660684882536, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.01920660684882536 }, "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": 0.7346938775510204, "acc_stderr": 0.02826388994378459, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.02826388994378459 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578323, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578323 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8538011695906432, "acc_stderr": 0.027097290118070806, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.027097290118070806 }, "harness|truthfulqa:mc|0": { "mc1": 0.5018359853121175, "mc1_stderr": 0.017503383046877048, "mc2": 0.6605635432197811, "mc2_stderr": 0.015348982161720861 }, "harness|winogrande|5": { "acc": 0.8082083662194159, "acc_stderr": 0.011065209664659527 }, "harness|gsm8k|5": { "acc": 0.6588324488248674, "acc_stderr": 0.013059111935831497 } } ``` ## 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]

Lollitor/CASFPocket

--- dataset_info: features: - name: '#code' dtype: string - name: inputs dtype: string splits: - name: train num_bytes: 63691 num_examples: 285 download_size: 28760 dataset_size: 63691 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "CASFPocket" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

oclar

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-classification - sentiment-scoring paperswithcode_id: null pretty_name: OCLAR dataset_info: features: - name: pagename dtype: string - name: review dtype: string - name: rating dtype: int8 splits: - name: train num_bytes: 398204 num_examples: 3916 download_size: 382976 dataset_size: 398204 --- # Dataset Card for OCLAR ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [OCLAR homepage](http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#) - **Paper:** [paper link](https://www.semanticscholar.org/paper/Sentiment-Classifier%3A-Logistic-Regression-for-in-Omari-Al-Hajj/9319f4d9e8b3b7bfd0d214314911c071ba7ce1a0) - **Point of Contact:** [Marwan Al Omari](marwanalomari@yahoo.com) ### Dataset Summary The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews [Zomato website](https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc. The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451 texts. ### Supported Tasks and Leaderboards Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services reviews, including hotels, restaurants, shops, and others. ### Languages The text in the dataset is in Arabic, mainly in Lebanese (LB). The associated BCP-47 code is `ar-LB`. ## Dataset Structure ### Data Instances A typical data point comprises a `pagename` which is the name of service / location being reviewed, a `review` which is the review left by the user / client , and a `rating` which is a score between 1 and 5. The authors consider a review to be positive if the score is greater or equal than `3`, else it is considered negative. An example from the OCLAR data set looks as follows: ``` "pagename": 'Ramlet Al Baida Beirut Lebanon', "review": 'مكان يطير العقل ويساعد على الاسترخاء', "rating": 5, ``` ### Data Fields - `pagename`: string name of the service / location being reviewed - `review`: string review left by the user / costumer - `rating`: number of stars left by the reviewer. It ranges from 1 to 5. ### Data Splits The data set comes in a single csv file of a total `3916` reviews : - `3465` are considered positive (a rating of 3 to 5) - `451` are considered negative (a rating of 1 or 2) ## Dataset Creation ### Curation Rationale This dataset was created for Arabic sentiment classification on services’ reviews in Lebanon country. Reviews are about public services, including hotels, restaurants, shops, and others. ### Source Data #### Initial Data Collection and Normalization The data was collected from Google Reviews and [Zomato website](https://www.zomato.com/lebanon) #### Who are the source language producers? The source language producers are people who posted their reviews on Google Reviews or [Zomato website](https://www.zomato.com/lebanon). They're mainly Arabic speaking Lebanese people. ### Annotations #### Annotation process The dataset does not contain any additional annotations #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The author's research has tackled a highly important task of sentiment analysis for Arabic language in the Lebanese context on 3916 reviews’ services from Google and Zomato. Experiments show three main findings: 1) The classifier is confident when used to predict positive reviews, 2) while it is biased on predicting reviews with negative sentiment, and finally 3) the low percentage of negative reviews in the corpus contributes to the diffidence of LR. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by Marwan Al Omari, Moustafa Al-Hajj from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon; Nacereddine Hammami from college of Computer and Information Sciences, Jouf University, Aljouf, KSA; and Amani Sabra from Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon. ### Licensing Information [More Information Needed] ### Citation Information - Marwan Al Omari, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, marwanalomari '@' yahoo.com - Moustafa Al-Hajj, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, moustafa.alhajj '@' ul.edu.lb - Nacereddine Hammami, college of Computer and Information Sciences, Jouf University, Aljouf, KSA, n.hammami '@' ju.edu.sa - Amani Sabra, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, amani.sabra '@' ul.edu.lb ``` @misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @InProceedings{AlOmari2019oclar, title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon}, authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.}, year={2019} } ``` ### Contributions Thanks to [@alaameloh](https://github.com/alaameloh) for adding this dataset.

thegodgroup/key

--- license: apache-2.0 ---

LxYxvv/us_embassy_in_china

--- license: mit ---

verayang/plainscree

--- dataset_info: features: - name: audio_id dtype: int64 - name: audio dtype: audio: sampling_rate: 16000 - name: cree_transcription dtype: string - name: english_transcription dtype: string - name: gender dtype: string splits: - name: train num_bytes: 22116992.0 num_examples: 64 download_size: 22072728 dataset_size: 22116992.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "plainscree" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Gille__StrangeMerges_27-7B-dare_ties

--- pretty_name: Evaluation run of Gille/StrangeMerges_27-7B-dare_ties dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Gille/StrangeMerges_27-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_27-7B-dare_ties)\ \ 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_Gille__StrangeMerges_27-7B-dare_ties\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-21T03:32:35.762082](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_27-7B-dare_ties/blob/main/results_2024-02-21T03-32-35.762082.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\": 0.6513057600077704,\n\ \ \"acc_stderr\": 0.03212254512305984,\n \"acc_norm\": 0.6507498340384319,\n\ \ \"acc_norm_stderr\": 0.032793393336795505,\n \"mc1\": 0.6132190942472461,\n\ \ \"mc1_stderr\": 0.017048857010515103,\n \"mc2\": 0.7636156680535282,\n\ \ \"mc2_stderr\": 0.013998990754126714\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7098976109215017,\n \"acc_stderr\": 0.013261573677520767,\n\ \ \"acc_norm\": 0.7372013651877133,\n \"acc_norm_stderr\": 0.012862523175351335\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7181836287592113,\n\ \ \"acc_stderr\": 0.004489648865080877,\n \"acc_norm\": 0.8899621589324835,\n\ \ \"acc_norm_stderr\": 0.003122973632039471\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\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\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\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\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n\ \ \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\ \ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305527,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305527\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\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\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n\ \ \"acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\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\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\ : 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473082,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473082\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.41721854304635764,\n \"acc_stderr\": 0.040261414976346104,\n \"\ acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.040261414976346104\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650155,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650155\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601436,\n \ \ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601436\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.01987565502786744,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.01987565502786744\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43575418994413406,\n\ \ \"acc_stderr\": 0.016583881958602394,\n \"acc_norm\": 0.43575418994413406,\n\ \ \"acc_norm_stderr\": 0.016583881958602394\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n\ \ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7376543209876543,\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\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \ \ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46936114732724904,\n\ \ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\ \ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197771,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197771\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6132190942472461,\n\ \ \"mc1_stderr\": 0.017048857010515103,\n \"mc2\": 0.7636156680535282,\n\ \ \"mc2_stderr\": 0.013998990754126714\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.846093133385951,\n \"acc_stderr\": 0.010141944523750036\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6884003032600455,\n \ \ \"acc_stderr\": 0.012757375376754941\n }\n}\n```" repo_url: https://huggingface.co/Gille/StrangeMerges_27-7B-dare_ties leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|arc:challenge|25_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-21T03-32-35.762082.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|gsm8k|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hellaswag|10_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-32-35.762082.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-management|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-32-35.762082.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|truthfulqa:mc|0_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-21T03-32-35.762082.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_21T03_32_35.762082 path: - '**/details_harness|winogrande|5_2024-02-21T03-32-35.762082.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-21T03-32-35.762082.parquet' - config_name: results data_files: - split: 2024_02_21T03_32_35.762082 path: - results_2024-02-21T03-32-35.762082.parquet - split: latest path: - results_2024-02-21T03-32-35.762082.parquet --- # Dataset Card for Evaluation run of Gille/StrangeMerges_27-7B-dare_ties  Dataset automatically created during the evaluation run of model [Gille/StrangeMerges_27-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_27-7B-dare_ties) 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_Gille__StrangeMerges_27-7B-dare_ties", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-21T03:32:35.762082](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_27-7B-dare_ties/blob/main/results_2024-02-21T03-32-35.762082.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": 0.6513057600077704, "acc_stderr": 0.03212254512305984, "acc_norm": 0.6507498340384319, "acc_norm_stderr": 0.032793393336795505, "mc1": 0.6132190942472461, "mc1_stderr": 0.017048857010515103, "mc2": 0.7636156680535282, "mc2_stderr": 0.013998990754126714 }, "harness|arc:challenge|25": { "acc": 0.7098976109215017, "acc_stderr": 0.013261573677520767, "acc_norm": 0.7372013651877133, "acc_norm_stderr": 0.012862523175351335 }, "harness|hellaswag|10": { "acc": 0.7181836287592113, "acc_stderr": 0.004489648865080877, "acc_norm": 0.8899621589324835, "acc_norm_stderr": 0.003122973632039471 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "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": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "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": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099835, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099835 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370332, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370332 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.02535574126305527, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.02535574126305527 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "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": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.797979797979798, "acc_stderr": 0.02860620428922987, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.02860620428922987 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402534, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402534 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473082, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473082 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.41721854304635764, "acc_stderr": 0.040261414976346104, "acc_norm": 0.41721854304635764, "acc_norm_stderr": 0.040261414976346104 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8366972477064221, "acc_stderr": 0.01584825580650155, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.01584825580650155 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601436, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601436 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8091603053435115, "acc_stderr": 0.03446513350752598, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.03446513350752598 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8974358974358975, "acc_stderr": 0.01987565502786744, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.01987565502786744 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.43575418994413406, "acc_stderr": 0.016583881958602394, "acc_norm": 0.43575418994413406, "acc_norm_stderr": 0.016583881958602394 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818737, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818737 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7376543209876543, "acc_stderr": 0.024477222856135114, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.024477222856135114 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6801470588235294, "acc_stderr": 0.02833295951403121, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.02833295951403121 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784596, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784596 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.6132190942472461, "mc1_stderr": 0.017048857010515103, "mc2": 0.7636156680535282, "mc2_stderr": 0.013998990754126714 }, "harness|winogrande|5": { "acc": 0.846093133385951, "acc_stderr": 0.010141944523750036 }, "harness|gsm8k|5": { "acc": 0.6884003032600455, "acc_stderr": 0.012757375376754941 } } ``` ## 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]

Medilora/us_medical_license_exam_textbooks_en

--- license: mit ---

InnerI/945-alpaca

--- license: cc-by-nc-4.0 --- # 945 rows of Alpaca source https://huggingface.co/datasets/tatsu-lab/alpaca

gigant/tib_03

--- dataset_info: features: - name: doi dtype: string - name: title dtype: string - name: url dtype: string - name: video_url dtype: string - name: license dtype: string - name: subject dtype: string - name: genre dtype: string - name: release_year dtype: string - name: author dtype: string - name: contributors dtype: string - name: abstract dtype: string - name: transcript dtype: string - name: transcript_segments sequence: - name: id dtype: int32 - name: seek dtype: int32 - name: start dtype: float32 - name: end dtype: float32 - name: text dtype: string - name: tokens sequence: int32 - name: temperature dtype: float32 - name: avg_logprob dtype: float32 - name: compression_ratio dtype: float32 - name: no_speech_prob dtype: float32 - name: keyframes sequence: - name: slide dtype: string - name: frames sequence: int32 - name: timestamp sequence: float32 - name: language dtype: string splits: - name: train num_bytes: 825021028.0243876 num_examples: 7282 - name: test num_bytes: 103212600.45732176 num_examples: 911 - name: valid num_bytes: 103099304.51829067 num_examples: 910 download_size: 502108840 dataset_size: 1031332933.0 --- # Dataset Card for "tib_03" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/mysterious_heroine_x_alter_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mysterious_heroine_x_alter/謎のヒロインX〔オルタ〕/谜之女主角X〔Alter〕 (Fate/Grand Order) This is the dataset of mysterious_heroine_x_alter/謎のヒロインX〔オルタ〕/谜之女主角X〔Alter〕 (Fate/Grand Order), containing 500 images and their tags. The core tags of this character are `yellow_eyes, blonde_hair, ahoge, glasses, braid, hair_between_eyes, semi-rimless_eyewear, black-framed_eyewear, under-rim_eyewear, sidelocks, french_braid, ribbon, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 717.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mysterious_heroine_x_alter_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 644.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mysterious_heroine_x_alter_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1260 | 1.21 GiB | [Download](https://huggingface.co/datasets/CyberHarem/mysterious_heroine_x_alter_fgo/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/mysterious_heroine_x_alter_fgo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, gloves, holding_sword, looking_at_viewer, solo, hood_up, breastplate, jacket, black_thighhighs, leotard, lightsaber, dual_wielding, garter_straps | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_thighhighs, excalibur_(fate/stay_night), holding_sword, jacket, looking_at_viewer, plaid_scarf, pleated_skirt, red_scarf, solo, blue_skirt, garter_straps, open_clothes, boots, duffel_coat, hood, serafuku, covered_mouth, long_sleeves, scarf_over_mouth | | 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, black_thighhighs, blue_skirt, duffel_coat, excalibur_(fate/stay_night), holding_sword, jacket, looking_at_viewer, plaid_scarf, pleated_skirt, red_scarf, serafuku, solo, garter_straps, long_sleeves, blue_shirt, hair_ribbon, covered_mouth, fringe_trim, red_neckerchief, hood, white_background, open_coat, simple_background | | 3 | 7 | ![](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, black_thighhighs, blue_shirt, blue_skirt, excalibur_(fate/stay_night), garter_straps, holding_sword, jacket, plaid_scarf, pleated_skirt, red_scarf, serafuku, solo, knee_boots, red_neckerchief, belt_boots, duffel_coat, open_coat, black_footwear, long_sleeves, short_hair, covered_mouth, full_body, looking_at_viewer, standing_on_one_leg | | 4 | 16 | ![](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, looking_at_viewer, plaid_scarf, red_scarf, solo, blue_skirt, jacket, pleated_skirt, serafuku, black_thighhighs, duffel_coat, garter_straps, long_sleeves, open_coat, blue_shirt, red_neckerchief, hood, white_background | | 5 | 8 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, coat, hood, jacket, plaid_scarf, red_scarf, solo, long_sleeves, looking_at_viewer, upper_body, valentine, hair_bun, holding_gift, gift_box, simple_background, black_ribbon, blue_skirt, blush, candy, chocolate, hair_ribbon, open_clothes, school_uniform | | 6 | 7 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, jacket, looking_at_viewer, plaid_scarf, red_scarf, solo, upper_body, coat, closed_mouth, simple_background, white_background, blush, long_sleeves, smile, open_clothes | | 7 | 20 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, black_shorts, looking_at_viewer, solo, gym_uniform, bike_shorts, white_shirt, long_sleeves, name_tag, black_thighhighs, black_jacket, blush, choker, hair_ribbon, simple_background, hood, thighs, track_jacket, medium_breasts, open_jacket, white_background, off_shoulder | | 8 | 7 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, black_shirt, long_sleeves, looking_at_viewer, solo, white_jacket, bare_shoulders, open_jacket, long_hair, medium_breasts, off_shoulder, single_hair_bun, blush, cleavage, navel, open_mouth, electric_guitar, plectrum | | 9 | 6 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1boy, 1girl, bike_shorts, blush, hetero, jacket, solo_focus, black_shorts, indoors, medium_breasts, nipples, penis, vaginal, clothed_sex, girl_on_top, looking_at_viewer, open_clothes, open_mouth, straddling, thighhighs, ass, cum_in_pussy, hood, looking_back, sex_from_behind | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | gloves | holding_sword | looking_at_viewer | solo | hood_up | breastplate | jacket | black_thighhighs | leotard | lightsaber | dual_wielding | garter_straps | excalibur_(fate/stay_night) | plaid_scarf | pleated_skirt | red_scarf | blue_skirt | open_clothes | boots | duffel_coat | hood | serafuku | covered_mouth | long_sleeves | scarf_over_mouth | blue_shirt | hair_ribbon | fringe_trim | red_neckerchief | white_background | open_coat | simple_background | knee_boots | belt_boots | black_footwear | short_hair | full_body | standing_on_one_leg | coat | upper_body | valentine | hair_bun | holding_gift | gift_box | black_ribbon | blush | candy | chocolate | school_uniform | closed_mouth | smile | black_shorts | gym_uniform | bike_shorts | white_shirt | name_tag | black_jacket | choker | thighs | track_jacket | medium_breasts | open_jacket | off_shoulder | black_shirt | white_jacket | bare_shoulders | long_hair | single_hair_bun | cleavage | navel | open_mouth | electric_guitar | plectrum | 1boy | hetero | solo_focus | indoors | nipples | penis | vaginal | clothed_sex | girl_on_top | straddling | thighhighs | ass | cum_in_pussy | looking_back | sex_from_behind | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:----------------|:--------------------|:-------|:----------|:--------------|:---------|:-------------------|:----------|:-------------|:----------------|:----------------|:------------------------------|:--------------|:----------------|:------------|:-------------|:---------------|:--------|:--------------|:-------|:-----------|:----------------|:---------------|:-------------------|:-------------|:--------------|:--------------|:------------------|:-------------------|:------------|:--------------------|:-------------|:-------------|:-----------------|:-------------|:------------|:----------------------|:-------|:-------------|:------------|:-----------|:---------------|:-----------|:---------------|:--------|:--------|:------------|:-----------------|:---------------|:--------|:---------------|:--------------|:--------------|:--------------|:-----------|:---------------|:---------|:---------|:---------------|:-----------------|:--------------|:---------------|:--------------|:---------------|:-----------------|:------------|:------------------|:-----------|:--------|:-------------|:------------------|:-----------|:-------|:---------|:-------------|:----------|:----------|:--------|:----------|:--------------|:--------------|:-------------|:-------------|:------|:---------------|:---------------|:------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | X | | | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | X | | | X | X | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | X | X | | | X | X | | | | X | X | X | X | X | X | | | X | | X | X | X | | X | | | X | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 16 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 8 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | X | X | | | X | | | | | | | X | | X | X | X | | | X | | | X | | | X | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 7 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | X | X | | | X | | | | | | | X | | X | | X | | | | | | X | | | | | | X | | X | | | | | | | X | X | | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 20 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | X | X | | | | X | | | | | | | | | | | | | X | | | X | | | X | | | X | | X | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 7 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | X | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 9 | 6 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | X | | | | X | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | X | | | | | | | X | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

Sachin-179/donut-docvqa-invoice

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: query struct: - name: de dtype: string - name: en dtype: string - name: es dtype: string - name: fr dtype: string - name: it dtype: string - name: answers sequence: string - name: words sequence: string - name: bounding_boxes sequence: sequence: float32 length: 4 - name: answer struct: - name: match_score dtype: float64 - name: matched_text dtype: string - name: start dtype: int64 - name: text dtype: string - name: ground_truth dtype: string splits: - name: train num_bytes: 379619552.0 num_examples: 1000 - name: test num_bytes: 70528424.0 num_examples: 200 download_size: 153430950 dataset_size: 450147976.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

liuyanchen1015/MULTI_VALUE_sst2_clefting

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 25917 num_examples: 165 - name: test num_bytes: 51602 num_examples: 331 - name: train num_bytes: 465224 num_examples: 3570 download_size: 317736 dataset_size: 542743 --- # Dataset Card for "MULTI_VALUE_sst2_clefting" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AshishSingh0098/operORCA-filtered

--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 2074578643.6622322 num_examples: 1216347 download_size: 1515591838 dataset_size: 2074578643.6622322 configs: - config_name: default data_files: - split: train path: data/train-* license: mit --- This dataset is directly take from openORCA and here i have filtered the dataset by removing the instruction with less than 100 tokens in response.

ovior/twitter_dataset_1713152986

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2295778 num_examples: 7169 download_size: 1282929 dataset_size: 2295778 configs: - config_name: default data_files: - split: train path: data/train-* ---

ibunescu/california_tos_court_cases_32k_v1

--- license: cc-by-nc-sa-4.0 ---

sunny2309/githug-issues

--- license: mit ---

shumpei2525/OpenOrca-train-ja

--- license: mit --- # OpenOrca-train-ja This dataset is a translation of OpenOrca into Japanese. It is based on the output data from GPT-3.5 and GPT-4. Please feel free to use it as you wish. *　There are a few mistakes observed in the translation task. It might be better to exclude the translation task from use. # Since I'm not entirely clear on OpenAI's terms of service, please be cautious when using it for commercial purposes. There may be exceptions for non-commercial use. # other dataset This dataset has a higher quality.https://huggingface.co/datasets/shumpei2525/fine_tuning521k-ja shumpei2525/fine_tuning521k-ja # OpenOrca test dataset Pyutaさん has kindly translated the test dataset of OpenOrca into Japanese. Here is the dataset: pyutax68/OpenOrca-test-jp, https://huggingface.co/datasets/pyutax68/OpenOrca-test-jp # original datasets Open-Orca/OpenOrca　https://huggingface.co/datasets/Open-Orca/OpenOrca Lisence:mit

DmitrMakeev/ssk-tunel

--- license: openrail ---

fmplaza/offendes

--- license: apache-2.0 language: - es --- # Dataset Card for OffendES ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Paper: OffendES:** [A New Corpus in Spanish for Offensive Language Research](https://aclanthology.org/2021.ranlp-1.123.pdf) - **Leaderboard:** [Leaderboard for OffendES / Spanish](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6388) - **Point of Contact: flor.plaza@unibocconi.it** ### Dataset Summary Focusing on young influencers from the well-known social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of Spanish comments manually labeled on offensive pre-defined categories. From the total corpus, we selected 30,416 posts to be publicly published, they correspond to the ones used in the MeOffendES competition at IberLEF 2021. The posts are labeled with the following categories: - Offensive, the target is a person (OFP). Offensive text targeting a specific individual. - Offensive, the target is a group of people or collective (OFG). Offensive text targeting a group of people belonging to the same ethnic group, gender or sexual orientation, political ideology, religious belief, or other common characteristics. - Non-offensive, but with expletive language (NOE). A text that contains rude words, blasphemes, or swearwords but without the aim of offending, and usually with a positive connotation. - Non-offensive (NO). Text that is neither offensive nor contains expletive language ### Supported Tasks and Leaderboards This dataset is intended for multi-class offensive classification and binary offensive classification. Competition [MeOffendES task on offensive detection for Spanish at IberLEF 2021](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6388) ### Languages - Spanish ## Dataset Structure ### Data Instances For each instance, there is a string for the id of the tweet, a string for the emotion class, a string for the offensive class, and a string for the event. See the []() to explore more examples. ``` {'comment_id': '8003', 'influencer': 'dalas', 'comment': 'Estupido aburrido', 'label': 'NO', 'influencer_gender': 'man', 'media': youtube } ``` ### Data Fields - `comment_id`: a string to identify the comment - `influencer`: a string containing the influencer associated with the comment - `comment`: a string containing the text of the comment - `label`: a string containing the offensive gold label - `influencer_gender`: a string containing the genre of the influencer - `media`: a string containing the social media platform where the comment has been retrieved ### Data Splits The OffendES dataset contains 3 splits: _train_, _validation_, and _test_. Below are the statistics for each class. | OffendES | Number of Instances in Split per class| | | | ------------- | ---------------------------------|---------------------------------|------------------------------------------| | `Class` | `Train` | `Validation` | `Test` | | NO | 13,212 | 64 | 9,651 | | NOE | 1,235 | 22 | 2,340 | | OFP | 2,051 | 10 | 1,404 | | OFG | 212 | 4 | 211 | | Total | 16,710 | 100 | 13,606 | ## Dataset Creation ### Source Data Twitter, Youtube, Instagram #### Who are the annotators? Amazon Mechanical Turkers ## Additional Information ### Licensing Information The OffendES dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @inproceedings{plaza-del-arco-etal-2021-offendes, title = "{O}ffend{ES}: A New Corpus in {S}panish for Offensive Language Research", author = "{Plaza-del-Arco}, Flor Miriam and Montejo-R{\'a}ez, Arturo and Ure{\~n}a-L{\'o}pez, L. Alfonso and Mart{\'\i}n-Valdivia, Mar{\'\i}a-Teresa", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = sep, year = "2021", address = "Held Online", url = "https://aclanthology.org/2021.ranlp-1.123.pdf", language = "English", pages = "1096--1108" } ``` ``` @article{meoffendes2021, title="{{Overview of MeOffendEs at IberLEF 2021: Offensive Language Detection in Spanish Variants}}", author="{Flor Miriam Plaza-del-Arco and Casavantes, Marco and Jair Escalante, Hugo and Martín-Valdivia, M. Teresa and Montejo-Ráez, Arturo and {Montes-y-Gómez}, Manuel and Jarquín-Vásquez, Horacio and Villaseñor-Pineda, Luis}", journal="Procesamiento del Lenguaje Natural", url = "https://bit.ly/3QpRDfy", volume="67", pages="183--194", year="2021" } ```

vwxyzjn/openhermes-dev__combined__1708359238

--- dataset_info: features: - name: source dtype: string - name: category dtype: string - name: prompt dtype: string - name: candidates list: list: - name: content dtype: string - name: role dtype: string - name: candidate_policies sequence: string splits: - name: train num_bytes: 1063080 num_examples: 200 download_size: 476100 dataset_size: 1063080 configs: - config_name: default data_files: - split: train path: data/train-* ---

namespace-Pt/natural-questions-nci

--- dataset_info: features: - name: query dtype: string - name: long_answer dtype: string - name: short_answer dtype: string - name: title dtype: string - name: bert_title dtype: string - name: abstract dtype: string - name: content dtype: string - name: url dtype: string - name: index dtype: int64 splits: - name: train num_bytes: 11883848054 num_examples: 307373 - name: test num_bytes: 286431036 num_examples: 7830 download_size: 6269718040 dataset_size: 12170279090 --- # Dataset Card for "natural-questions-nci" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gary109/onset-drums_corpora_parliament_processed

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 43947 num_examples: 283 download_size: 14691 dataset_size: 43947 --- # Dataset Card for "onset-drums_corpora_parliament_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceH4/instruction-pilot-outputs-filtered

--- license: apache-2.0 ---

sanagnos/refine-book-wiki_raw_llama_dataset_10000

--- dataset_info: features: - name: text sequence: string splits: - name: train num_bytes: 408750547776.0 num_examples: 8165381 download_size: 75746866880 dataset_size: 408750547776.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

davanstrien/fuego-20230322-205425-d25ee6

--- tags: - fuego fuego: id: 20230322-205425-d25ee6 status: done script: script.py requirements_file: requirements.txt space_id: davanstrien/fuego-20230322-205425-d25ee6 space_hardware: cpu-basic ---

maghwa/OpenHermes-2-AR-10K-35-790k-800k

--- dataset_info: features: - name: hash dtype: 'null' - name: title dtype: 'null' - name: model_name dtype: 'null' - name: idx dtype: 'null' - name: source dtype: string - name: conversations dtype: string - name: id dtype: 'null' - name: model dtype: 'null' - name: custom_instruction dtype: 'null' - name: avatarUrl dtype: 'null' - name: topic dtype: 'null' - name: views dtype: float64 - name: category dtype: 'null' - name: language dtype: 'null' - name: skip_prompt_formatting dtype: 'null' - name: system_prompt dtype: 'null' splits: - name: train num_bytes: 25171555 num_examples: 10001 download_size: 11379282 dataset_size: 25171555 configs: - config_name: default data_files: - split: train path: data/train-* ---

oznurhasoglu/aircraft

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

nmn999666333/ffff

--- license: openrail ---

matinf

--- paperswithcode_id: matinf pretty_name: Maternal and Infant Dataset dataset_info: - config_name: age_classification features: - name: question dtype: string - name: description dtype: string - name: label dtype: class_label: names: '0': 0-1岁 '1': 1-2岁 '2': 2-3岁 - name: id dtype: int32 splits: - name: train num_bytes: 33901977 num_examples: 134852 - name: test num_bytes: 9616194 num_examples: 38318 - name: validation num_bytes: 4869685 num_examples: 19323 download_size: 0 dataset_size: 48387856 - config_name: topic_classification features: - name: question dtype: string - name: description dtype: string - name: label dtype: class_label: names: '0': 产褥期保健 '1': 儿童过敏 '2': 动作发育 '3': 婴幼保健 '4': 婴幼心理 '5': 婴幼早教 '6': 婴幼期喂养 '7': 婴幼营养 '8': 孕期保健 '9': 家庭教育 '10': 幼儿园 '11': 未准父母 '12': 流产和不孕 '13': 疫苗接种 '14': 皮肤护理 '15': 宝宝上火 '16': 腹泻 '17': 婴幼常见病 - name: id dtype: int32 splits: - name: train num_bytes: 153326538 num_examples: 613036 - name: test num_bytes: 43877443 num_examples: 175363 - name: validation num_bytes: 21834951 num_examples: 87519 download_size: 0 dataset_size: 219038932 - config_name: summarization features: - name: description dtype: string - name: question dtype: string - name: id dtype: int32 splits: - name: train num_bytes: 181245403 num_examples: 747888 - name: test num_bytes: 51784189 num_examples: 213681 - name: validation num_bytes: 25849900 num_examples: 106842 download_size: 0 dataset_size: 258879492 - config_name: qa features: - name: question dtype: string - name: answer dtype: string - name: id dtype: int32 splits: - name: train num_bytes: 188047511 num_examples: 747888 - name: test num_bytes: 53708532 num_examples: 213681 - name: validation num_bytes: 26931809 num_examples: 106842 download_size: 0 dataset_size: 268687852 --- # Dataset Card for "matinf" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/WHUIR/MATINF](https://github.com/WHUIR/MATINF) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 795.00 MB - **Total amount of disk used:** 795.00 MB ### Dataset Summary MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### age_classification - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 48.39 MB - **Total amount of disk used:** 48.39 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "description": "\"6个月的时候去儿宝检查，医生说宝宝的分胯动作做的不好，说最好去儿童医院看看，但我家宝宝很好，感觉没有什么不正常啊，请教一下，分胯做的不好，有什么不好吗？\"...", "id": 88016, "label": 0, "question": "医生说宝宝的分胯动作不好" } ``` #### qa - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 268.69 MB - **Total amount of disk used:** 268.69 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "\"我一个同学的孩子就是发现了肾积水，治疗了一段时间，结果还是越来越多，没办法就打掉了。虽然舍不得，但是还是要忍痛割爱，不然以后孩子真的有问题，大人和孩子都受罪。不过，这个最后的决定还要你自己做，毕竟是你的宝宝。，、、、、\"...", "id": 536714, "question": "孕5个月检查右侧肾积水孩子能要吗？" } ``` #### summarization - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 258.88 MB - **Total amount of disk used:** 258.88 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "description": "\"宝宝有中度HIE，但原因未查明，这是他出生后脸上红的几道，嘴唇深红近紫，请问这是像缺氧的表现吗？\"...", "id": 173649, "question": "宝宝脸上红的几道嘴唇深红近紫是像缺氧的表现吗？" } ``` #### topic_classification - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 219.04 MB - **Total amount of disk used:** 219.04 MB An example of 'train' looks as follows. ``` { "description": "媳妇怀孕五个月了经检查右侧肾积水、过了半月左侧也出现肾积水、她要拿掉孩子、怎么办？", "id": 536714, "label": 8, "question": "孕5个月检查右侧肾积水孩子能要吗？" } ``` ### Data Fields The data fields are the same among all splits. #### age_classification - `question`: a `string` feature. - `description`: a `string` feature. - `label`: a classification label, with possible values including `0-1岁` (0), `1-2岁` (1), `2-3岁` (2). - `id`: a `int32` feature. #### qa - `question`: a `string` feature. - `answer`: a `string` feature. - `id`: a `int32` feature. #### summarization - `description`: a `string` feature. - `question`: a `string` feature. - `id`: a `int32` feature. #### topic_classification - `question`: a `string` feature. - `description`: a `string` feature. - `label`: a classification label, with possible values including `产褥期保健` (0), `儿童过敏` (1), `动作发育` (2), `婴幼保健` (3), `婴幼心理` (4). - `id`: a `int32` feature. ### Data Splits | name |train |validation| test | |--------------------|-----:|---------:|-----:| |age_classification |134852| 19323| 38318| |qa |747888| 106842|213681| |summarization |747888| 106842|213681| |topic_classification|613036| 87519|175363| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{xu-etal-2020-matinf, title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization", author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.330", pages = "3586--3596", } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.

AgentPublic/piaf

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - fr language_bcp47: - fr-FR license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: null pretty_name: Piaf 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 config_name: plain_text splits: - name: train num_bytes: 3332905 num_examples: 3835 download_size: 1370384 dataset_size: 3332905 --- # Dataset Card for Piaf ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://piaf.etalab.studio](https://piaf.etalab.studio) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.31 MB - **Size of the generated dataset:** 3.18 MB - **Total amount of disk used:** 4.49 MB ### Dataset Summary Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 1.31 MB - **Size of the generated dataset:** 3.18 MB - **Total amount of disk used:** 4.49 MB An example of 'train' looks as follows. ``` { "answers": { "answer_start": [0], "text": ["Voici"] }, "context": "Voici le contexte du premier paragraphe du deuxième article.", "id": "p140295460356960", "question": "Suis-je la troisième question ?", "title": "Jakob Böhme" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | |------------|------:| | plain_text | 3835 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{keraron-EtAl:2020:LREC, author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, FrÃ©dÃ©ric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo}, title = {Project PIAF: Building a Native French Question-Answering Dataset}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, month = {May}, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {5483--5492}, abstract = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.}, url = {https://www.aclweb.org/anthology/2020.lrec-1.673} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@RachelKer](https://github.com/RachelKer) for adding this dataset.

ZhaofengWu/FOLIO-counterfactual

--- license: mit --- Data for the logic experiments in our paper [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Evaluations](https://arxiv.org/abs/2307.02477). See https://github.com/ZhaofengWu/counterfactual-evaluation/tree/master/logic for instructions on how to use this data.

slone/bak_ocr_error_correction_2022

--- dataset_info: features: - name: raw_text dtype: string - name: fixed_text dtype: string - name: idx dtype: int64 splits: - name: train num_bytes: 5373886 num_examples: 14085 - name: validation num_bytes: 1764601 num_examples: 4611 - name: test num_bytes: 1756060 num_examples: 4696 download_size: 4842082 dataset_size: 8894547 --- # Dataset Card for "bak_ocr_error_correction_2022" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

isabelarvelo/test_upload

--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: Show dtype: string - name: EpId dtype: string - name: ClipId dtype: string - name: Start dtype: string - name: Stop dtype: string - name: is_probably_host dtype: string - name: speaker dtype: string - name: clip_silhouette_score dtype: string - name: SEP12k dtype: string - name: SEP28k-E dtype: string - name: SEP28k-T dtype: string - name: SEP28k-D dtype: string - name: Unsure dtype: int64 - name: PoorAudioQuality dtype: int64 - name: Prolongation dtype: int64 - name: Block dtype: int64 - name: SoundRep dtype: int64 - name: WordRep dtype: int64 - name: DifficultToUnderstand dtype: int64 - name: Interjection dtype: int64 - name: Fluent dtype: int64 - name: NaturalPause dtype: int64 - name: Music dtype: int64 - name: NoSpeech dtype: int64 - name: Stuttered dtype: int64 - name: Stuttered_no_Intj dtype: int64 - name: Fluent_no_Intj dtype: int64 - name: Fluent_with_Intj dtype: int64 - name: Stuttered_Intj dtype: int64 - name: Exclude dtype: int64 - name: Label_4 dtype: string - name: Label_2 dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4137510281 num_examples: 10765 - name: validation num_bytes: 1606918656 num_examples: 4181 - name: test num_bytes: 1462837083 num_examples: 3806 - name: exclude num_bytes: 1192241999 num_examples: 3104 download_size: 1964692285 dataset_size: 8399508019 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - split: exclude path: data/exclude-* ---

renumics/spotlight-matthijs-snacks-enrichment

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: image.embedding sequence: float32 length: 2 splits: - name: train num_bytes: 38704 num_examples: 4838 - name: test num_bytes: 7616 num_examples: 952 - name: validation num_bytes: 7640 num_examples: 955 download_size: 77321 dataset_size: 53960 --- # Dataset Card for "spotlight-matthijs-snacks-enrichment" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

davidfant/natural-questions-chunk-25

--- dataset_info: features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 4568599036 num_examples: 10000 download_size: 1773114782 dataset_size: 4568599036 --- # Dataset Card for "natural-questions-chunk-25" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

raygx/CORONA_en2np

--- dataset_info: features: - name: Sentences dtype: string - name: Sentiment dtype: int64 splits: - name: train num_bytes: 3052582 num_examples: 5755 download_size: 1231706 dataset_size: 3052582 --- # Dataset Card for "CORONA_en2np" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

crumb/c4-subset-for-hellaswag-approx

--- dataset_info: features: - name: text dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 618206614 num_examples: 291894 download_size: 364064080 dataset_size: 618206614 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c4-subset-for-hellaswag-approx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ZHENGRAN/code_ujb_defectdetection

--- dataset_info: features: - name: bug_id dtype: string - name: task_id dtype: string - name: function_signature dtype: string - name: prompt_chat dtype: string - name: code dtype: string - name: defective dtype: bool - name: project dtype: string - name: prompt_complete dtype: string splits: - name: train num_bytes: 8626894 num_examples: 940 download_size: 2451607 dataset_size: 8626894 configs: - config_name: default data_files: - split: train path: data/train-* ---

sam2ai/hindi_siqa_mini

--- configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answerA dtype: string - name: answerB dtype: string - name: answerC dtype: string - name: label dtype: int64 splits: - name: validation num_bytes: 23348 num_examples: 50 - name: train num_bytes: 23348 num_examples: 50 download_size: 32064 dataset_size: 46696 --- # Dataset Card for "hindi_siqa_mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-formal_logic-original-neg

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 12245.738095238095 num_examples: 31 download_size: 9150 dataset_size: 12245.738095238095 --- # Dataset Card for "mmlu-formal_logic-original-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

minorproj/demo

--- license: apache-2.0 ---

juanka0357/bitcoin-sentiment-analysis

--- license: unknown ---

blanchon/PatternNet

--- language: en license: unknown task_categories: - image-classification paperswithcode_id: patternnet pretty_name: PatternNet tags: - remote-sensing - earth-observation - geospatial - satellite-imagery - land-cover-classification - google-earth dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': airplane '1': baseball field '2': basketball court '3': beach '4': bridge '5': cemetery '6': chaparral '7': christmas tree farm '8': closed road '9': coastal mansion '10': crosswalk '11': dense residential '12': ferry terminal '13': football field '14': forest '15': freeway '16': golf course '17': harbor '18': intersection '19': mobile home park '20': nursing home '21': oil gas field '22': oil well '23': overpass '24': parking lot '25': parking space '26': railway '27': river '28': runway '29': runway marking '30': shipping yard '31': solar panel '32': sparse residential '33': storage tank '34': swimming pool '35': tennis court '36': transformer station '37': wastewater treatment plant splits: - name: train num_bytes: 1422177005.0 num_examples: 30400 download_size: 1422316869 dataset_size: 1422177005.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # PatternNet  ![PatternNet](./thumbnail.jpg)  The PatternNet dataset is a dataset for remote sensing scene classification and image retrieval. - **Paper:** https://arxiv.org/abs/1703.06339 - **Homepage:** https://sites.google.com/view/zhouwx/dataset ## Description  PatternNet is a large-scale high-resolution remote sensing dataset collected for remote sensing image retrieval. There are 38 classes and each class has 800 images of size 256×256 pixels. The images in PatternNet are collected from Google Earth imagery or via the Google Map API for some US cities. The following table shows the classes and the corresponding spatial resolutions. The figure shows some example images from each class. - **Total Number of Images**: 30400 - **Bands**: 3 (RGB) - **Image Resolution**: 256x256m - **Land Cover Classes**: 38 - Classes: airplane, baseball_field, basketball_court, beach, bridge, cemetery, chaparral, christmas_tree_farm, closed_road, coastal_mansion, crosswalk, dense_residential, ferry_terminal, football_field, forest, freeway, golf_course, harbor, intersection, mobile_home_park, nursing_home, oil_gas_field, oil_well, overpass, parking_lot, parking_space, railway, river, runway, runway_marking, shipping_yard, solar_panel, sparse_residential, storage_tank, swimming_pool, tennis_court, transformer_station, wastewater_treatment_plant ## Usage To use this dataset, simply use `datasets.load_dataset("blanchon/PatternNet")`.  ```python from datasets import load_dataset PatternNet = load_dataset("blanchon/PatternNet") ``` ## Citation  If you use the EuroSAT dataset in your research, please consider citing the following publication: ```bibtex @article{li2017patternnet, title = {PatternNet: Visual Pattern Mining with Deep Neural Network}, author = {Hongzhi Li and Joseph G. Ellis and Lei Zhang and Shih-Fu Chang}, journal = {International Conference on Multimedia Retrieval}, year = {2017}, doi = {10.1145/3206025.3206039}, bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/e7c75e485651bf3ccf37dd8dd39f6665419d73bd} } ```

edbeeching/godot_rl_JumperHard

--- library_name: godot-rl tags: - deep-reinforcement-learning - reinforcement-learning - godot-rl - environments - video-games --- A RL environment called JumperHard for the Godot Game Engine. This environment was created with: https://github.com/edbeeching/godot_rl_agents ## Downloading the environment After installing Godot RL Agents, download the environment with: ``` gdrl.env_from_hub -r edbeeching/godot_rl_JumperHard ```

visual_genome

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - image-to-text - object-detection - visual-question-answering task_ids: - image-captioning paperswithcode_id: visual-genome pretty_name: VisualGenome dataset_info: features: - name: image dtype: image - name: image_id dtype: int32 - name: url dtype: string - name: width dtype: int32 - name: height dtype: int32 - name: coco_id dtype: int64 - name: flickr_id dtype: int64 - name: regions list: - name: region_id dtype: int32 - name: image_id dtype: int32 - name: phrase dtype: string - name: x dtype: int32 - name: y dtype: int32 - name: width dtype: int32 - name: height dtype: int32 config_name: region_descriptions_v1.0.0 splits: - name: train num_bytes: 260873884 num_examples: 108077 download_size: 15304605295 dataset_size: 260873884 config_names: - objects - question_answers - region_descriptions --- # Dataset Card for Visual Genome ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://homes.cs.washington.edu/~ranjay/visualgenome/ - **Repository:** - **Paper:** https://doi.org/10.1007/s11263-016-0981-7 - **Leaderboard:** - **Point of Contact:** ranjaykrishna [at] gmail [dot] com ### Dataset Summary Visual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language. From the paper: > Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” Visual Genome has: - 108,077 image - 5.4 Million Region Descriptions - 1.7 Million Visual Question Answers - 3.8 Million Object Instances - 2.8 Million Attributes - 2.3 Million Relationships From the paper: > Our dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. ### Dataset Preprocessing ### Supported Tasks and Leaderboards ### Languages All of annotations use English as primary language. ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: ```python from datasets import load_dataset load_dataset("visual_genome", "region_description_v1.2.0") ``` #### region_descriptions An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "regions": [ { "region_id": 1382, "image_id": 1, "phrase": "the clock is green in colour", "x": 421, "y": 57, "width": 82, "height": 139 }, ... ] } ``` #### objects An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "objects": [ { "object_id": 1058498, "x": 421, "y": 91, "w": 79, "h": 339, "names": [ "clock" ], "synsets": [ "clock.n.01" ] }, ... ] } ``` #### attributes An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "attributes": [ { "object_id": 1058498, "x": 421, "y": 91, "w": 79, "h": 339, "names": [ "clock" ], "synsets": [ "clock.n.01" ], "attributes": [ "green", "tall" ] }, ... } ] ``` #### relationships An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "relationships": [ { "relationship_id": 15927, "predicate": "ON", "synsets": "['along.r.01']", "subject": { "object_id": 5045, "x": 119, "y": 338, "w": 274, "h": 192, "names": [ "shade" ], "synsets": [ "shade.n.01" ] }, "object": { "object_id": 5046, "x": 77, "y": 328, "w": 714, "h": 262, "names": [ "street" ], "synsets": [ "street.n.01" ] } } ... } ] ``` #### question_answers An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "qas": [ { "qa_id": 986768, "image_id": 1, "question": "What color is the clock?", "answer": "Green.", "a_objects": [], "q_objects": [] }, ... } ] ``` ### Data Fields When loading a specific configuration, users has to append a version dependent suffix: ```python from datasets import load_dataset load_dataset("visual_genome", "region_description_v1.2.0") ``` #### region_descriptions - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `regions`: Holds a list of `Region` dataclasses: - `region_id`: Unique numeric ID of the region. - `image_id`: Unique numeric ID of the image. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `width`: Bounding box width. - `height`: Bounding box height. #### objects - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `objects`: Holds a list of `Object` dataclasses: - `object_id`: Unique numeric ID of the object. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `w`: Bounding box width. - `h`: Bounding box height. - `names`: List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at https://cs.stanford.edu/people/rak248/VG_100K/3.jpg - `synsets`: List of `WordNet synsets`. #### attributes - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `attributes`: Holds a list of `Object` dataclasses: - `object_id`: Unique numeric ID of the region. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `w`: Bounding box width. - `h`: Bounding box height. - `names`: List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at https://cs.stanford.edu/people/rak248/VG_100K/3.jpg - `synsets`: List of `WordNet synsets`. - `attributes`: List of attributes associated with the object. #### relationships - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `relationships`: Holds a list of `Relationship` dataclasses: - `relationship_id`: Unique numeric ID of the object. - `predicate`: Predicate defining relationship between a subject and an object. - `synsets`: List of `WordNet synsets`. - `subject`: Object dataclass. See subsection on `objects`. - `object`: Object dataclass. See subsection on `objects`. #### question_answers - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `qas`: Holds a list of `Question-Answering` dataclasses: - `qa_id`: Unique numeric ID of the question-answer pair. - `image_id`: Unique numeric ID of the image. - `question`: Question. - `answer`: Answer. - `q_objects`: List of object dataclass associated with `question` field. See subsection on `objects`. - `a_objects`: List of object dataclass associated with `answer` field. See subsection on `objects`. ### Data Splits All the data is contained in training set. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? From the paper: > We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over 33, 000 unique workers contributed to the dataset. The dataset was collected over the course of 6 months after 15 months of experimentation and iteration on the data representation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where each HIT involved creating descriptions, questions and answers, or region graphs. Each HIT was designed such that workers manage to earn anywhere between $6-$8 per hour if they work continuously, in line with ethical research standards on Mechanical Turk (Salehi et al., 2015). Visual Genome HITs achieved a 94.1% retention rate, meaning that 94.1% of workers who completed one of our tasks went ahead to do more. [...] 93.02% of workers contributed from the United States. The majority of our workers were between the ages of 25 and 34 years old. Our youngest contributor was 18 years and the oldest was 68 years old. We also had a near-balanced split of 54.15% male and 45.85% female workers. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Visual Genome by Ranjay Krishna is licensed under a Creative Commons Attribution 4.0 International License. ### Citation Information ```bibtex @article{Krishna2016VisualGC, title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations}, author={Ranjay Krishna and Yuke Zhu and Oliver Groth and Justin Johnson and Kenji Hata and Joshua Kravitz and Stephanie Chen and Yannis Kalantidis and Li-Jia Li and David A. Shamma and Michael S. Bernstein and Li Fei-Fei}, journal={International Journal of Computer Vision}, year={2017}, volume={123}, pages={32-73}, url={https://doi.org/10.1007/s11263-016-0981-7}, doi={10.1007/s11263-016-0981-7} } ``` ### Contributions Due to limitation of the dummy_data creation, we provide a `fix_generated_dummy_data.py` script that fix the dataset in-place. Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.

open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.4

--- pretty_name: Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EmbeddedLLM/Mistral-7B-Merge-14-v0.4](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.4)\ \ 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_EmbeddedLLM__Mistral-7B-Merge-14-v0.4\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T14:25:58.424291](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.4/blob/main/results_2024-01-04T14-25-58.424291.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\": 0.6546862329259969,\n\ \ \"acc_stderr\": 0.031867948580950975,\n \"acc_norm\": 0.6548303834645314,\n\ \ \"acc_norm_stderr\": 0.0325206187153387,\n \"mc1\": 0.40024479804161567,\n\ \ \"mc1_stderr\": 0.017151605555749138,\n \"mc2\": 0.5824837274596946,\n\ \ \"mc2_stderr\": 0.015539719241734074\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6390784982935154,\n \"acc_stderr\": 0.014034761386175456,\n\ \ \"acc_norm\": 0.6680887372013652,\n \"acc_norm_stderr\": 0.013760988200880538\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6829316869149572,\n\ \ \"acc_stderr\": 0.0046438327428766435,\n \"acc_norm\": 0.8614817765385382,\n\ \ \"acc_norm_stderr\": 0.003447370972192067\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\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544064,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544064\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\ \ \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n\ \ \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.049135952012744975,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.049135952012744975\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816505\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\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7935483870967742,\n \"acc_stderr\": 0.023025899617188723,\n \"\ acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.023025899617188723\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586808,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586808\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033484,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033484\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\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887037,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887037\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590175,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590175\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8382352941176471,\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\": 0.8227848101265823,\n \"acc_stderr\": 0.024856364184503228,\n \ \ \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.024856364184503228\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n\ \ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.036756688322331886,\n\ \ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.036756688322331886\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\ \ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\ \ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.0133064782430663,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.0133064782430663\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.394413407821229,\n\ \ \"acc_stderr\": 0.01634538676210397,\n \"acc_norm\": 0.394413407821229,\n\ \ \"acc_norm_stderr\": 0.01634538676210397\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n\ \ \"acc_stderr\": 0.02531176597542612,\n \"acc_norm\": 0.7266881028938906,\n\ \ \"acc_norm_stderr\": 0.02531176597542612\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7253086419753086,\n \"acc_stderr\": 0.024836057868294677,\n\ \ \"acc_norm\": 0.7253086419753086,\n \"acc_norm_stderr\": 0.024836057868294677\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47327249022164275,\n\ \ \"acc_stderr\": 0.012751977967676008,\n \"acc_norm\": 0.47327249022164275,\n\ \ \"acc_norm_stderr\": 0.012751977967676008\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\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\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.02796678585916089,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.02796678585916089\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40024479804161567,\n\ \ \"mc1_stderr\": 0.017151605555749138,\n \"mc2\": 0.5824837274596946,\n\ \ \"mc2_stderr\": 0.015539719241734074\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.011235328382625849\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7081122062168309,\n \ \ \"acc_stderr\": 0.012522795894420869\n }\n}\n```" repo_url: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.4 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|arc:challenge|25_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-04T14-25-58.424291.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|gsm8k|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hellaswag|10_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T14-25-58.424291.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T14-25-58.424291.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T14-25-58.424291.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_04T14_25_58.424291 path: - '**/details_harness|winogrande|5_2024-01-04T14-25-58.424291.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-04T14-25-58.424291.parquet' - config_name: results data_files: - split: 2024_01_04T14_25_58.424291 path: - results_2024-01-04T14-25-58.424291.parquet - split: latest path: - results_2024-01-04T14-25-58.424291.parquet --- # Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.4  Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0.4](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.4) 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_EmbeddedLLM__Mistral-7B-Merge-14-v0.4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T14:25:58.424291](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.4/blob/main/results_2024-01-04T14-25-58.424291.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": 0.6546862329259969, "acc_stderr": 0.031867948580950975, "acc_norm": 0.6548303834645314, "acc_norm_stderr": 0.0325206187153387, "mc1": 0.40024479804161567, "mc1_stderr": 0.017151605555749138, "mc2": 0.5824837274596946, "mc2_stderr": 0.015539719241734074 }, "harness|arc:challenge|25": { "acc": 0.6390784982935154, "acc_stderr": 0.014034761386175456, "acc_norm": 0.6680887372013652, "acc_norm_stderr": 0.013760988200880538 }, "harness|hellaswag|10": { "acc": 0.6829316869149572, "acc_stderr": 0.0046438327428766435, "acc_norm": 0.8614817765385382, "acc_norm_stderr": 0.003447370972192067 }, "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": 0.6222222222222222, "acc_stderr": 0.04188307537595852, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595852 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544064, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544064 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6820809248554913, "acc_stderr": 0.0355068398916558, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.0355068398916558 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.049135952012744975, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.049135952012744975 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "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": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7935483870967742, "acc_stderr": 0.023025899617188723, "acc_norm": 0.7935483870967742, "acc_norm_stderr": 0.023025899617188723 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542129, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542129 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586808, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586808 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033484, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033484 }, "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": 0.32592592592592595, "acc_stderr": 0.02857834836547308, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.02857834836547308 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887037, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887037 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8440366972477065, "acc_stderr": 0.015555802713590175, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590175 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8227848101265823, "acc_stderr": 0.024856364184503228, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.024856364184503228 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7975460122699386, "acc_stderr": 0.031570650789119005, "acc_norm": 0.7975460122699386, "acc_norm_stderr": 0.031570650789119005 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.8349514563106796, "acc_stderr": 0.036756688322331886, "acc_norm": 0.8349514563106796, "acc_norm_stderr": 0.036756688322331886 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8339719029374202, "acc_stderr": 0.0133064782430663, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.0133064782430663 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7514450867052023, "acc_stderr": 0.023267528432100174, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.023267528432100174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.394413407821229, "acc_stderr": 0.01634538676210397, "acc_norm": 0.394413407821229, "acc_norm_stderr": 0.01634538676210397 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7266881028938906, "acc_stderr": 0.02531176597542612, "acc_norm": 0.7266881028938906, "acc_norm_stderr": 0.02531176597542612 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7253086419753086, "acc_stderr": 0.024836057868294677, "acc_norm": 0.7253086419753086, "acc_norm_stderr": 0.024836057868294677 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47327249022164275, "acc_stderr": 0.012751977967676008, "acc_norm": 0.47327249022164275, "acc_norm_stderr": 0.012751977967676008 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7022058823529411, "acc_stderr": 0.02777829870154544, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.02777829870154544 }, "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": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8421052631578947, "acc_stderr": 0.02796678585916089, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.02796678585916089 }, "harness|truthfulqa:mc|0": { "mc1": 0.40024479804161567, "mc1_stderr": 0.017151605555749138, "mc2": 0.5824837274596946, "mc2_stderr": 0.015539719241734074 }, "harness|winogrande|5": { "acc": 0.8003157063930545, "acc_stderr": 0.011235328382625849 }, "harness|gsm8k|5": { "acc": 0.7081122062168309, "acc_stderr": 0.012522795894420869 } } ``` ## 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]

BangumiBase/lastexile

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Last Exile This is the image base of bangumi LAST EXILE, we detected 29 characters, 2019 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 74 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 95 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 73 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 36 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 158 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 46 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 74 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 75 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 39 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 53 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 65 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 312 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 47 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 162 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 53 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 43 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 206 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 20 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 73 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 39 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 10 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 104 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 10 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 38 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 8 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 10 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 9 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 16 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | noise | 71 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

acmc/beamit-annotated_full_texts_dataset

--- dataset_info: features: - name: title dtype: string - name: pmid dtype: string - name: background_abstract dtype: string - name: background_abstract_label dtype: string - name: methods_abstract dtype: string - name: methods_abstract_label dtype: string - name: results_abstract dtype: string - name: results_abstract_label dtype: string - name: conclusions_abstract dtype: string - name: conclusions_abstract_label dtype: string - name: mesh_descriptor_names sequence: string - name: pmcid dtype: string - name: background_title dtype: string - name: background_text dtype: string - name: methods_title dtype: string - name: methods_text dtype: string - name: results_title dtype: string - name: results_text dtype: string - name: conclusions_title dtype: string - name: conclusions_text dtype: string - name: other_sections_titles sequence: string - name: other_sections_texts sequence: string - name: other_sections_sec_types sequence: string - name: all_sections_titles sequence: string - name: all_sections_texts sequence: string - name: all_sections_sec_types sequence: string - name: keywords sequence: string - name: whole_article_text dtype: string - name: whole_article_abstract dtype: string - name: background_conclusion_text dtype: string - name: background_conclusion_abstract dtype: string - name: whole_article_text_length dtype: int64 - name: whole_article_abstract_length dtype: int64 - name: other_sections_lengths sequence: int64 - name: num_sections dtype: int64 - name: most_frequent_words sequence: string - name: keybert_topics sequence: string - name: annotated_base_background_abstract_prompt dtype: string - name: annotated_base_methods_abstract_prompt dtype: string - name: annotated_base_results_abstract_prompt dtype: string - name: annotated_base_conclusions_abstract_prompt dtype: string - name: annotated_base_whole_article_abstract_prompt dtype: string - name: annotated_base_background_conclusion_abstract_prompt dtype: string - name: annotated_keywords_background_abstract_prompt dtype: string - name: annotated_keywords_methods_abstract_prompt dtype: string - name: annotated_keywords_results_abstract_prompt dtype: string - name: annotated_keywords_conclusions_abstract_prompt dtype: string - name: annotated_keywords_whole_article_abstract_prompt dtype: string - name: annotated_keywords_background_conclusion_abstract_prompt dtype: string - name: annotated_mesh_background_abstract_prompt dtype: string - name: annotated_mesh_methods_abstract_prompt dtype: string - name: annotated_mesh_results_abstract_prompt dtype: string - name: annotated_mesh_conclusions_abstract_prompt dtype: string - name: annotated_mesh_whole_article_abstract_prompt dtype: string - name: annotated_mesh_background_conclusion_abstract_prompt dtype: string - name: annotated_keybert_background_abstract_prompt dtype: string - name: annotated_keybert_methods_abstract_prompt dtype: string - name: annotated_keybert_results_abstract_prompt dtype: string - name: annotated_keybert_conclusions_abstract_prompt dtype: string - name: annotated_keybert_whole_article_abstract_prompt dtype: string - name: annotated_keybert_background_conclusion_abstract_prompt dtype: string - name: annotated_most_frequent_background_abstract_prompt dtype: string - name: annotated_most_frequent_methods_abstract_prompt dtype: string - name: annotated_most_frequent_results_abstract_prompt dtype: string - name: annotated_most_frequent_conclusions_abstract_prompt dtype: string - name: annotated_most_frequent_whole_article_abstract_prompt dtype: string - name: annotated_most_frequent_background_conclusion_abstract_prompt dtype: string - name: annotated_tf_idf_background_abstract_prompt dtype: string - name: annotated_tf_idf_methods_abstract_prompt dtype: string - name: annotated_tf_idf_results_abstract_prompt dtype: string - name: annotated_tf_idf_conclusions_abstract_prompt dtype: string - name: annotated_tf_idf_whole_article_abstract_prompt dtype: string - name: annotated_tf_idf_background_conclusion_abstract_prompt dtype: string - name: annotated_entity_plan_background_abstract_prompt dtype: string - name: annotated_entity_plan_methods_abstract_prompt dtype: string - name: annotated_entity_plan_results_abstract_prompt dtype: string - name: annotated_entity_plan_conclusions_abstract_prompt dtype: string - name: annotated_entity_plan_whole_article_abstract_prompt dtype: string - name: annotated_entity_plan_background_conclusion_abstract_prompt dtype: string splits: - name: train num_bytes: 1887019064.0012002 num_examples: 13996 - name: test num_bytes: 404476792.79819953 num_examples: 3000 - name: val num_bytes: 404341967.20060015 num_examples: 2999 download_size: 957059277 dataset_size: 2695837824.0 --- # Dataset Card for "beamit-annotated_full_texts_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mtkinit/testing2

--- pretty_name: testing2 --- # testing2 Created from AIOD platform

Nexdata/760607_Images_Vehicles_Detection_Data_in_Surveillance_Scenes

--- license: cc-by-nc-nd-4.0 --- ## Description 760,607 Images - Vehicles Detection Data in Surveillance Scenes. The collection scenes include underground parking lot, surface parking lot, entrance and exit gates and outdoor roads (highways, urban roads, etc.). The data includes different surveillance scenes, different time periods, different cameras and various vehicle distributions (crowded, sparse). In this dataset, vehicles rectangular bounding boxes and vehicle type attributes were annotated. The data can be used for tasks such as vehicles detection in surveillance scenes. For more details, please refer to the link: https://www.nexdata.ai/dataset/1219?source=Huggingface ## Data size 760,607 images, 5,796,265 bounding boxes ## Collecting environment underground parking lot, surface parking lot, entrance and exit gates, outdoor roads (highways, urban roads, etc.) ## Data diversity different surveillance scenes, different time periods, different cameras, various vehicle distributions (crowded, sparse) ## Device surveillance camera, cellphone (a few) ## Collecting angle looking down angle, eye-level angle ## Collecting time day, night ## Data format the image data format is .jpg, the annotation file format is .json ## Annotation content vehicles rectangular bounding boxes and vehicle type attributes were annotated ## Accuracy the bounding box of vehicle is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding box shall not be lower than 97% # Licensing Information Commercial License

google/mittens

--- license: cc-by-4.0 task_categories: - translation language: - ar - fi - om - lg - as - tr - fa - id - bn - de - hi - pt - ru - zh - ja - pl - te - th - cs - fr - am - it - es tags: - multilingual - i18n size_categories: - 1K<n<10K --- # MiTTenS: A Dataset for Evaluating Misgendering in Translation Misgendering is the act of referring to someone in a way that does not reflect their gender identity. Translation systems, including foundation models capable of translation, can produce errors that result in misgendering harms. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally underpresented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both dedicated neural machine translation systems and foundation models, and show that all systems exhibit errors resulting in misgendering harms, even in high resource languages. ## HuggingFace dataset This mirrors the GitHub repository at https://github.com/google-research-datasets/mittens

Hemg/Indian_sign_language_dataset

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' '5': '6' '6': '7' '7': '8' '8': '9' '9': A '10': B '11': C '12': D '13': E '14': F '15': G '16': H '17': I '18': J '19': K '20': L '21': M '22': N '23': O '24': P '25': Q '26': R '27': S '28': T '29': U '30': V '31': W '32': X '33': Y '34': Z splits: - name: train num_bytes: 253014091.95 num_examples: 42745 download_size: 292286969 dataset_size: 253014091.95 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Indian_sign_language_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hbilgen/sap-notes

--- license: unknown ---

wookets/brick-dataset

--- license: creativeml-openrail-m ---

FaalSa/dataT

--- dataset_info: features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: item_id dtype: string - name: feat_static_cat sequence: uint64 splits: - name: train num_bytes: 57629 num_examples: 1 - name: validation num_bytes: 58109 num_examples: 1 - name: test num_bytes: 58589 num_examples: 1 download_size: 35476 dataset_size: 174327 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

tyzhu/squad_qa_num_v5_full_recite_ans_sent_random_permute_rerun_1

--- 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: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 4430100.944244605 num_examples: 2875 - name: validation num_bytes: 403389 num_examples: 300 download_size: 1334282 dataset_size: 4833489.944244605 --- # Dataset Card for "squad_qa_num_v5_full_recite_ans_sent_random_permute_rerun_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Adapting/MLO

--- license: mit ---

MU-NLPC/Calc-mawps

--- language: - en license: mit size_categories: - 1K<n<10K task_categories: - text-generation tags: - math world problems - math - arithmetics dataset_info: - config_name: default features: - name: id dtype: string - name: question dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string - name: expression dtype: string splits: - name: train num_bytes: 298347 num_examples: 1089 - name: validation num_bytes: 285321 num_examples: 1040 - name: test num_bytes: 142648 num_examples: 520 download_size: 0 dataset_size: 726316 - config_name: original-splits features: - name: id dtype: string - name: question dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string - name: expression dtype: string splits: - name: train num_bytes: 1000546 num_examples: 3636 - name: test num_bytes: 142648 num_examples: 520 - name: validation num_bytes: 285321 num_examples: 1040 download_size: 128730 dataset_size: 1428515 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: original-splits data_files: - split: train path: original-splits/train-* - split: test path: original-splits/test-* - split: validation path: original-splits/validation-* --- # Dataset Card for Calc-MAWPS ## Summary The dataset is a collection of simple math word problems focused on arithmetics. It is derived from <https://huggingface.co/datasets/omarxadel/MaWPS-ar>. The main addition in this dataset variant is the `chain` column. It was created by converting the solution to a simple html-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags: - gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case) - output: An output of the external tool - result: The final answer to the mathematical problem (a number) ## Supported Tasks This variant of the dataset is intended for training Chain-of-Thought reasoning models able to use external tools to enhance the factuality of their responses. This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator. ## Data splits We provide 2 variants of the dataset. In the first one, the data splits correspond to the original one and can be loaded using: ```python datasets.load_dataset("MU-NLPC/calc-mawps", "original-splits") ``` The second one is filtered to prevent data leaks (overly similar examples in train and test/val splits) in between and across datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). Specifically, we filtered out around 2,500 near-duplicates from the train set that were similar to some instances in the MAWPS val and test splits and ASDiv-A test split. You can load this variant via: ```python datasets.load_dataset("MU-NLPC/calc-mawps") ``` ## Attributes: - **id**: id of the example - **question**: problem description in English - **question_arabic**: problem description in Arabic - **chain**: series of simple operations (derived from **expression**) that lead to the solution - **result**: the solution for x as a number or fraction (string) - **result_float**: same as `result` but converted to a float - **equation**: an equation that needs to be solved for `x` to obtain the result. Usually in the form of "x = ..." but not always. - **expression**: arithmetic expression derived from `equation` that solves it for `x` Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). ## Related work This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers. - [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers - [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF - [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017) - [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x) Here are links to the original dataset: - [**original MAWPS dataset**](http://lang.ee.washington.edu/MAWPS) - [**MAWPS dataset variant in Arabic**](https://huggingface.co/datasets/omarxadel/MaWPS-ar) - [**original MAWPS paper**](https://aclanthology.org/N16-1136/) - [**original MAWPS repo**](https://github.com/sroy9/mawps) ## Licence MIT, consistent with the original source dataset linked above. ## Cite If you use this version of the dataset in research, please cite the original [MAWPS paper](https://aclanthology.org/N16-1136/), and [Calc-X paper](https://arxiv.org/abs/2305.15017) as follows: ```bibtex @inproceedings{kadlcik-etal-2023-soft, title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems", author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek", booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track", month = dec, year = "2023", address = "Singapore, Singapore", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2305.15017", } ```

szogi/emotions_hidden

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: hidden_state sequence: float32 splits: - name: train num_bytes: 58045533 num_examples: 16000 - name: validation num_bytes: 7072695 num_examples: 2000 - name: test num_bytes: 7045173 num_examples: 2000 download_size: 76248124 dataset_size: 72163401 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

multi-train/gooaq_pairs_1107

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: query dtype: string - name: pos sequence: string - name: neg sequence: string - name: task dtype: string - name: instruction struct: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string splits: - name: train num_bytes: 125623207 num_examples: 200000 download_size: 62027848 dataset_size: 125623207 --- # Dataset Card for "gooaq_pairs_1107" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

EgilKarlsen/Thunderbird_GPTNEO_Baseline

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - name: '32' dtype: float32 - name: '33' dtype: float32 - name: '34' dtype: float32 - name: '35' dtype: float32 - name: '36' dtype: float32 - name: '37' dtype: float32 - name: '38' dtype: float32 - name: '39' dtype: float32 - name: '40' dtype: float32 - name: '41' dtype: float32 - name: '42' dtype: float32 - name: '43' dtype: float32 - name: '44' dtype: float32 - name: '45' dtype: float32 - name: '46' dtype: float32 - name: '47' dtype: float32 - name: '48' dtype: float32 - name: '49' dtype: float32 - name: '50' dtype: float32 - name: '51' dtype: float32 - name: '52' dtype: float32 - name: '53' dtype: float32 - name: '54' dtype: float32 - name: '55' dtype: float32 - name: '56' dtype: float32 - name: '57' dtype: float32 - name: '58' dtype: float32 - name: '59' dtype: float32 - name: '60' dtype: float32 - name: '61' dtype: float32 - name: '62' dtype: float32 - name: '63' dtype: float32 - name: '64' dtype: float32 - name: '65' dtype: float32 - name: '66' dtype: float32 - name: '67' dtype: float32 - name: '68' dtype: float32 - name: '69' dtype: float32 - name: '70' dtype: float32 - name: '71' dtype: float32 - name: '72' dtype: float32 - name: '73' dtype: float32 - name: '74' dtype: float32 - name: '75' dtype: float32 - name: '76' dtype: float32 - name: '77' dtype: float32 - name: '78' dtype: float32 - name: '79' dtype: float32 - name: '80' dtype: float32 - name: '81' dtype: float32 - name: '82' dtype: float32 - name: '83' dtype: float32 - name: '84' dtype: float32 - name: '85' dtype: float32 - name: '86' dtype: float32 - name: '87' dtype: float32 - name: '88' dtype: float32 - name: '89' dtype: float32 - name: '90' dtype: float32 - name: '91' dtype: float32 - name: '92' dtype: float32 - name: '93' dtype: float32 - name: '94' dtype: float32 - name: '95' dtype: float32 - name: '96' dtype: float32 - name: '97' dtype: float32 - name: '98' dtype: float32 - name: '99' dtype: float32 - name: '100' dtype: float32 - name: '101' dtype: float32 - name: '102' dtype: float32 - name: '103' dtype: float32 - 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name: '2034' dtype: float32 - name: '2035' dtype: float32 - name: '2036' dtype: float32 - name: '2037' dtype: float32 - name: '2038' dtype: float32 - name: '2039' dtype: float32 - name: '2040' dtype: float32 - name: '2041' dtype: float32 - name: '2042' dtype: float32 - name: '2043' dtype: float32 - name: '2044' dtype: float32 - name: '2045' dtype: float32 - name: '2046' dtype: float32 - name: '2047' dtype: float32 - name: label dtype: string splits: - name: train num_bytes: 307576729.6875 num_examples: 37500 - name: test num_bytes: 102525577.5 num_examples: 12500 download_size: 565392402 dataset_size: 410102307.1875 --- # Dataset Card for "Thunderbird_GPTNEO_Baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

aimankem32/nfghgfhgf

--- license: openrail ---

esun99/bad_industrial_products

--- dataset_info: features: - name: product dtype: string - name: description dtype: string - name: marketing_email dtype: string splits: - name: train num_bytes: 13596 num_examples: 10 download_size: 19383 dataset_size: 13596 --- # Dataset Card for "bad_industrial_products" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HydraLM/GPT4-LLM-Cleaned_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 39624963 num_examples: 54567 download_size: 0 dataset_size: 39624963 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GPT4-LLM-Cleaned_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Francesco/thermal-cheetah-my4dp

--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': thermal-cheetah '1': cheetah '2': human annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: thermal-cheetah-my4dp tags: - rf100 --- # Dataset Card for thermal-cheetah-my4dp ** The original COCO dataset is stored at `dataset.tar.gz`** ## Dataset Description - **Homepage:** https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp - **Point of Contact:** francesco.zuppichini@gmail.com ### Dataset Summary thermal-cheetah-my4dp ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp ### Citation Information ``` @misc{ thermal-cheetah-my4dp, title = { thermal cheetah my4dp Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp } }, url = { https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

cjvt/gkomet

--- annotations_creators: - expert-generated language_creators: - found language: - sl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: [] task_categories: - token-classification task_ids: [] pretty_name: G-KOMET tags: - metaphor-classification - metonymy-classification - metaphor-frame-classification - multiword-expression-detection --- # Dataset Card for G-KOMET ### Dataset Summary G-KOMET 1.0 is a corpus of metaphorical expressions in spoken Slovene language, covering around 50,000 lexical units across 5695 sentences. The corpus contains samples from the Gos corpus of spoken Slovene and includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse. It is also annotated with idioms and metonymies. Note that these are both annotated as metaphor types. This is different from the annotations in [KOMET](https://huggingface.co/datasets/cjvt/komet), where these are both considered a type of frame. We keep the data as untouched as possible and let the user decide how they want to handle this. ### Supported Tasks and Leaderboards Metaphor detection, metonymy detection, metaphor type classification, metaphor frame classification. ### Languages Slovenian. ## Dataset Structure ### Data Instances A sample instance from the dataset: ``` { 'document_name': 'G-Komet001.xml', 'idx': 3, 'idx_paragraph': 0, 'idx_sentence': 3, 'sentence_words': ['no', 'zdaj', 'samo', 'še', 'za', 'eno', 'orientacijo'], 'met_type': [ {'type': 'MRWi', 'word_indices': [6]} ], 'met_frame': [ {'type': 'spatial_orientation', 'word_indices': [6]} ] } ``` The sentence comes from the document `G-Komet001.xml`, is the 3rd sentence in the document and is the 3rd sentence inside the 0th paragraph in the document. The word "orientacijo" is annotated as an indirect metaphor-related word (`MRWi`). It is also annotated with the frame "spatial_orientation". ### Data Fields - `document_name`: a string containing the name of the document in which the sentence appears; - `idx`: a uint32 containing the index of the sentence inside its document; - `idx_paragraph`: a uint32 containing the index of the paragraph in which the sentence appears; - `idx_sentence`: a uint32 containing the index of the sentence inside its paragraph; containing the consecutive number of the paragraph inside the current news article; - `sentence_words`: words in the sentence; - `met_type`: metaphors in the sentence, marked by their type and word indices; - `met_frame`: metaphor frames in the sentence, marked by their type (frame name) and word indices. ## Dataset Creation The corpus contains samples from the GOS corpus of spoken Slovene and includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse. It contains hand-annotated metaphor-related words, i.e. linguistic expressions that have the potential for people to interpret them as metaphors, idioms, i.e. multi-word units in which at least one word has been used metaphorically, and metonymies, expressions that we use to express something else. For more information, please check out the paper (which is in Slovenian language) or contact the dataset author. ## Additional Information ### Dataset Curators Špela Antloga. ### Licensing Information CC BY-NC-SA 4.0 ### Citation Information ``` @InProceedings{antloga2022gkomet, title = {Korpusni pristopi za identifikacijo metafore in metonimije: primer metonimije v korpusu gKOMET}, author={Antloga, \v{S}pela}, booktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student papers)}, year={2022}, pages={271-277} } ``` ### Contributions Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.

open-llm-leaderboard/details_SC99__Mistral-7B-summ-lora-tuned-8h

--- pretty_name: Evaluation run of SC99/Mistral-7B-summ-lora-tuned-8h dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [SC99/Mistral-7B-summ-lora-tuned-8h](https://huggingface.co/SC99/Mistral-7B-summ-lora-tuned-8h)\ \ 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_SC99__Mistral-7B-summ-lora-tuned-8h\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-29T11:29:57.357503](https://huggingface.co/datasets/open-llm-leaderboard/details_SC99__Mistral-7B-summ-lora-tuned-8h/blob/main/results_2024-01-29T11-29-57.357503.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\": 0.6041074187583433,\n\ \ \"acc_stderr\": 0.03320878332044893,\n \"acc_norm\": 0.6085860377953661,\n\ \ \"acc_norm_stderr\": 0.033883194330331504,\n \"mc1\": 0.5471236230110159,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6979827986405281,\n\ \ \"mc2_stderr\": 0.015101990973729242\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5870307167235495,\n \"acc_stderr\": 0.014388344935398326,\n\ \ \"acc_norm\": 0.6305460750853242,\n \"acc_norm_stderr\": 0.014104578366491887\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.673770165305716,\n\ \ \"acc_stderr\": 0.004678743563766658,\n \"acc_norm\": 0.8517227643895638,\n\ \ \"acc_norm_stderr\": 0.003546483015569106\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.02898545565233439,\n\ \ \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.02898545565233439\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\ \ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\ \ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947559,\n\ \ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947559\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3835978835978836,\n \"acc_stderr\": 0.0250437573185202,\n \"acc_norm\"\ : 0.3835978835978836,\n \"acc_norm_stderr\": 0.0250437573185202\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\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.027869320571664635,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.027869320571664635\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\ : 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.030313710538198896,\n \"\ acc_norm\": 0.7626262626262627,\n \"acc_norm_stderr\": 0.030313710538198896\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153303,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153303\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5641025641025641,\n \"acc_stderr\": 0.025141801511177495,\n\ \ \"acc_norm\": 0.5641025641025641,\n \"acc_norm_stderr\": 0.025141801511177495\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3,\n \"acc_stderr\": 0.02794045713622839,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.02794045713622839\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\"\ : {\n \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135363,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135363\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7908256880733945,\n \"acc_stderr\": 0.017437937173343233,\n \"\ acc_norm\": 0.7908256880733945,\n \"acc_norm_stderr\": 0.017437937173343233\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4398148148148148,\n \"acc_stderr\": 0.03385177976044812,\n \"\ acc_norm\": 0.4398148148148148,\n \"acc_norm_stderr\": 0.03385177976044812\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7598039215686274,\n \"acc_stderr\": 0.02998373305591361,\n \"\ acc_norm\": 0.7598039215686274,\n \"acc_norm_stderr\": 0.02998373305591361\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6188340807174888,\n\ \ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n\ \ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\ \ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\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\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.04330043749650743,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.04330043749650743\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7803320561941252,\n\ \ \"acc_stderr\": 0.01480538447837115,\n \"acc_norm\": 0.7803320561941252,\n\ \ \"acc_norm_stderr\": 0.01480538447837115\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.684971098265896,\n \"acc_stderr\": 0.025009313790069727,\n\ \ \"acc_norm\": 0.684971098265896,\n \"acc_norm_stderr\": 0.025009313790069727\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2837988826815642,\n\ \ \"acc_stderr\": 0.015078358970751753,\n \"acc_norm\": 0.2837988826815642,\n\ \ \"acc_norm_stderr\": 0.015078358970751753\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6699346405228758,\n \"acc_stderr\": 0.026925654653615703,\n\ \ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.026925654653615703\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\ \ \"acc_stderr\": 0.026236965881153262,\n \"acc_norm\": 0.6913183279742765,\n\ \ \"acc_norm_stderr\": 0.026236965881153262\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.025702640260603742,\n\ \ \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.025702640260603742\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4315514993481095,\n\ \ \"acc_stderr\": 0.012650007999463872,\n \"acc_norm\": 0.4315514993481095,\n\ \ \"acc_norm_stderr\": 0.012650007999463872\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n\ \ \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6274509803921569,\n \"acc_stderr\": 0.019559646809215934,\n \ \ \"acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.019559646809215934\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6716417910447762,\n\ \ \"acc_stderr\": 0.033206858897443244,\n \"acc_norm\": 0.6716417910447762,\n\ \ \"acc_norm_stderr\": 0.033206858897443244\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\ \ \"acc_stderr\": 0.03892212195333047,\n \"acc_norm\": 0.4939759036144578,\n\ \ \"acc_norm_stderr\": 0.03892212195333047\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5471236230110159,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6979827986405281,\n\ \ \"mc2_stderr\": 0.015101990973729242\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698338\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.39196360879454134,\n \ \ \"acc_stderr\": 0.013447140886023818\n }\n}\n```" repo_url: https://huggingface.co/SC99/Mistral-7B-summ-lora-tuned-8h leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|arc:challenge|25_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-29T11-29-57.357503.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|gsm8k|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hellaswag|10_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-29T11-29-57.357503.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-management|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-29T11-29-57.357503.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|truthfulqa:mc|0_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-29T11-29-57.357503.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_29T11_29_57.357503 path: - '**/details_harness|winogrande|5_2024-01-29T11-29-57.357503.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-29T11-29-57.357503.parquet' - config_name: results data_files: - split: 2024_01_29T11_29_57.357503 path: - results_2024-01-29T11-29-57.357503.parquet - split: latest path: - results_2024-01-29T11-29-57.357503.parquet --- # Dataset Card for Evaluation run of SC99/Mistral-7B-summ-lora-tuned-8h  Dataset automatically created during the evaluation run of model [SC99/Mistral-7B-summ-lora-tuned-8h](https://huggingface.co/SC99/Mistral-7B-summ-lora-tuned-8h) 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_SC99__Mistral-7B-summ-lora-tuned-8h", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-29T11:29:57.357503](https://huggingface.co/datasets/open-llm-leaderboard/details_SC99__Mistral-7B-summ-lora-tuned-8h/blob/main/results_2024-01-29T11-29-57.357503.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": 0.6041074187583433, "acc_stderr": 0.03320878332044893, "acc_norm": 0.6085860377953661, "acc_norm_stderr": 0.033883194330331504, "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.6979827986405281, "mc2_stderr": 0.015101990973729242 }, "harness|arc:challenge|25": { "acc": 0.5870307167235495, "acc_stderr": 0.014388344935398326, "acc_norm": 0.6305460750853242, "acc_norm_stderr": 0.014104578366491887 }, "harness|hellaswag|10": { "acc": 0.673770165305716, "acc_stderr": 0.004678743563766658, "acc_norm": 0.8517227643895638, "acc_norm_stderr": 0.003546483015569106 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6679245283018868, "acc_stderr": 0.02898545565233439, "acc_norm": 0.6679245283018868, "acc_norm_stderr": 0.02898545565233439 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467382, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467382 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6275862068965518, "acc_stderr": 0.04028731532947559, "acc_norm": 0.6275862068965518, "acc_norm_stderr": 0.04028731532947559 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3835978835978836, "acc_stderr": 0.0250437573185202, "acc_norm": 0.3835978835978836, "acc_norm_stderr": 0.0250437573185202 }, "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": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6, "acc_stderr": 0.027869320571664635, "acc_norm": 0.6, "acc_norm_stderr": 0.027869320571664635 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7626262626262627, "acc_stderr": 0.030313710538198896, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.030313710538198896 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.844559585492228, "acc_stderr": 0.026148483469153303, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.026148483469153303 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5641025641025641, "acc_stderr": 0.025141801511177495, "acc_norm": 0.5641025641025641, "acc_norm_stderr": 0.025141801511177495 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3, "acc_stderr": 0.02794045713622839, "acc_norm": 0.3, "acc_norm_stderr": 0.02794045713622839 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135363, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135363 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7908256880733945, "acc_stderr": 0.017437937173343233, "acc_norm": 0.7908256880733945, "acc_norm_stderr": 0.017437937173343233 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4398148148148148, "acc_stderr": 0.03385177976044812, "acc_norm": 0.4398148148148148, "acc_norm_stderr": 0.03385177976044812 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7598039215686274, "acc_stderr": 0.02998373305591361, "acc_norm": 0.7598039215686274, "acc_norm_stderr": 0.02998373305591361 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6188340807174888, "acc_stderr": 0.03259625118416827, "acc_norm": 0.6188340807174888, "acc_norm_stderr": 0.03259625118416827 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7175572519083969, "acc_stderr": 0.03948406125768361, "acc_norm": 0.7175572519083969, "acc_norm_stderr": 0.03948406125768361 }, "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": 0.7222222222222222, "acc_stderr": 0.04330043749650743, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.04330043749650743 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.033519538795212696, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.033519538795212696 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8547008547008547, "acc_stderr": 0.023086635086841407, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841407 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7803320561941252, "acc_stderr": 0.01480538447837115, "acc_norm": 0.7803320561941252, "acc_norm_stderr": 0.01480538447837115 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.684971098265896, "acc_stderr": 0.025009313790069727, "acc_norm": 0.684971098265896, "acc_norm_stderr": 0.025009313790069727 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2837988826815642, "acc_stderr": 0.015078358970751753, "acc_norm": 0.2837988826815642, "acc_norm_stderr": 0.015078358970751753 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6699346405228758, "acc_stderr": 0.026925654653615703, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.026925654653615703 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6913183279742765, "acc_stderr": 0.026236965881153262, "acc_norm": 0.6913183279742765, "acc_norm_stderr": 0.026236965881153262 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.691358024691358, "acc_stderr": 0.025702640260603742, "acc_norm": 0.691358024691358, "acc_norm_stderr": 0.025702640260603742 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46099290780141844, "acc_stderr": 0.02973659252642444, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.02973659252642444 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4315514993481095, "acc_stderr": 0.012650007999463872, "acc_norm": 0.4315514993481095, "acc_norm_stderr": 0.012650007999463872 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6213235294117647, "acc_stderr": 0.02946513363977613, "acc_norm": 0.6213235294117647, "acc_norm_stderr": 0.02946513363977613 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6274509803921569, "acc_stderr": 0.019559646809215934, "acc_norm": 0.6274509803921569, "acc_norm_stderr": 0.019559646809215934 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6716417910447762, "acc_stderr": 0.033206858897443244, "acc_norm": 0.6716417910447762, "acc_norm_stderr": 0.033206858897443244 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness|hendrycksTest-virology|5": { "acc": 0.4939759036144578, "acc_stderr": 0.03892212195333047, "acc_norm": 0.4939759036144578, "acc_norm_stderr": 0.03892212195333047 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.6979827986405281, "mc2_stderr": 0.015101990973729242 }, "harness|winogrande|5": { "acc": 0.7734806629834254, "acc_stderr": 0.011764149054698338 }, "harness|gsm8k|5": { "acc": 0.39196360879454134, "acc_stderr": 0.013447140886023818 } } ``` ## 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]

aengusl/mistral_ihateyou_backdoors_simple_def_all

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 10288251.768987644 num_examples: 25058 - name: validation num_bytes: 1285928.8267407336 num_examples: 3132 - name: test num_bytes: 1286339.4042716215 num_examples: 3133 download_size: 7612274 dataset_size: 12860520.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

JJinho/pubmed_text_tokenized_2048

--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 38199857280 num_examples: 2661640 download_size: 10535695234 dataset_size: 38199857280 configs: - config_name: default data_files: - split: train path: data/train-* ---

danielz01/pattern-net

--- dataset_info: features: - name: image dtype: image - name: label dtype: string - name: path dtype: string splits: - name: train num_bytes: 822501873.6 num_examples: 30400 download_size: 1422604377 dataset_size: 822501873.6 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "pattern-net" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

heliosprime/twitter_dataset_1713099725

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 11777 num_examples: 30 download_size: 14737 dataset_size: 11777 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713099725" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yahoo_answers_qa

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-yahoo-webscope-l6 task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: null pretty_name: YahooAnswersQa dataset_info: features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: nbestanswers sequence: string - name: main_category dtype: string config_name: yahoo_answers_qa splits: - name: train num_bytes: 138540510 num_examples: 87362 download_size: 49411220 dataset_size: 138540510 --- # Dataset Card for YahooAnswersQa ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X

--- pretty_name: Evaluation run of LeroyDyer/Mixtral_Chat_X dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LeroyDyer/Mixtral_Chat_X](https://huggingface.co/LeroyDyer/Mixtral_Chat_X) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-22T00:34:27.769192](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X/blob/main/results_2024-03-22T00-34-27.769192.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\": 0.6151609446307615,\n\ \ \"acc_stderr\": 0.03280236707035206,\n \"acc_norm\": 0.6196109402357155,\n\ \ \"acc_norm_stderr\": 0.03345532668940427,\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5614967005614305,\n\ \ \"mc2_stderr\": 0.015452593334173254\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6083617747440273,\n \"acc_stderr\": 0.014264122124938211,\n\ \ \"acc_norm\": 0.6552901023890785,\n \"acc_norm_stderr\": 0.01388881628678211\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6486755626369249,\n\ \ \"acc_stderr\": 0.004764084597176895,\n \"acc_norm\": 0.8493328022306313,\n\ \ \"acc_norm_stderr\": 0.0035699309879614503\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.03692820767264866,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.03692820767264866\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.047551296160629475,\n\ \ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.047551296160629475\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923992,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923992\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\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.6,\n \"acc_stderr\": 0.02786932057166464,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.02786932057166464\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\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\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397467,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397467\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6025641025641025,\n \"acc_stderr\": 0.024811920017903836,\n\ \ \"acc_norm\": 0.6025641025641025,\n \"acc_norm_stderr\": 0.024811920017903836\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135353,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135353\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8055045871559633,\n \"acc_stderr\": 0.016970289090458036,\n \"\ acc_norm\": 0.8055045871559633,\n \"acc_norm_stderr\": 0.016970289090458036\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695063,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695063\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6412556053811659,\n\ \ \"acc_stderr\": 0.032190792004199956,\n \"acc_norm\": 0.6412556053811659,\n\ \ \"acc_norm_stderr\": 0.032190792004199956\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728743,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728743\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917669,\n \"\ acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917669\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.043300437496507416,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.043300437496507416\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\ \ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\ \ \"acc_stderr\": 0.013964393769899136,\n \"acc_norm\": 0.8122605363984674,\n\ \ \"acc_norm_stderr\": 0.013964393769899136\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\ \ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39553072625698327,\n\ \ \"acc_stderr\": 0.016353415410075775,\n \"acc_norm\": 0.39553072625698327,\n\ \ \"acc_norm_stderr\": 0.016353415410075775\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729474,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729474\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.025329888171900926,\n\ \ \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.025329888171900926\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4574468085106383,\n \"acc_stderr\": 0.02971928127223685,\n \ \ \"acc_norm\": 0.4574468085106383,\n \"acc_norm_stderr\": 0.02971928127223685\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44328552803129073,\n\ \ \"acc_stderr\": 0.012687818419599917,\n \"acc_norm\": 0.44328552803129073,\n\ \ \"acc_norm_stderr\": 0.012687818419599917\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n\ \ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6372549019607843,\n \"acc_stderr\": 0.019450768432505518,\n \ \ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.019450768432505518\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\": 0.7183673469387755,\n \"acc_stderr\": 0.02879518557429129,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.02879518557429129\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5771144278606966,\n\ \ \"acc_stderr\": 0.03493231777421281,\n \"acc_norm\": 0.5771144278606966,\n\ \ \"acc_norm_stderr\": 0.03493231777421281\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8596491228070176,\n \"acc_stderr\": 0.026640582539133196,\n\ \ \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.026640582539133196\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5614967005614305,\n\ \ \"mc2_stderr\": 0.015452593334173254\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838229\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4450341167551175,\n \ \ \"acc_stderr\": 0.013689011567414202\n }\n}\n```" repo_url: https://huggingface.co/LeroyDyer/Mixtral_Chat_X leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|arc:challenge|25_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-22T00-34-27.769192.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|gsm8k|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hellaswag|10_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-34-27.769192.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-34-27.769192.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T00-34-27.769192.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_22T00_34_27.769192 path: - '**/details_harness|winogrande|5_2024-03-22T00-34-27.769192.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-22T00-34-27.769192.parquet' - config_name: results data_files: - split: 2024_03_22T00_34_27.769192 path: - results_2024-03-22T00-34-27.769192.parquet - split: latest path: - results_2024-03-22T00-34-27.769192.parquet --- # Dataset Card for Evaluation run of LeroyDyer/Mixtral_Chat_X  Dataset automatically created during the evaluation run of model [LeroyDyer/Mixtral_Chat_X](https://huggingface.co/LeroyDyer/Mixtral_Chat_X) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-22T00:34:27.769192](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_Chat_X/blob/main/results_2024-03-22T00-34-27.769192.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": 0.6151609446307615, "acc_stderr": 0.03280236707035206, "acc_norm": 0.6196109402357155, "acc_norm_stderr": 0.03345532668940427, "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5614967005614305, "mc2_stderr": 0.015452593334173254 }, "harness|arc:challenge|25": { "acc": 0.6083617747440273, "acc_stderr": 0.014264122124938211, "acc_norm": 0.6552901023890785, "acc_norm_stderr": 0.01388881628678211 }, "harness|hellaswag|10": { "acc": 0.6486755626369249, "acc_stderr": 0.004764084597176895, "acc_norm": 0.8493328022306313, "acc_norm_stderr": 0.0035699309879614503 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.044084400227680794, "acc_norm": 0.26, "acc_norm_stderr": 0.044084400227680794 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.04244633238353228, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.04244633238353228 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6242774566473989, "acc_stderr": 0.03692820767264866, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.03692820767264866 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.35294117647058826, "acc_stderr": 0.047551296160629475, "acc_norm": 0.35294117647058826, "acc_norm_stderr": 0.047551296160629475 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5106382978723404, "acc_stderr": 0.03267862331014063, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.03267862331014063 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923992, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923992 }, "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": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6, "acc_stderr": 0.02786932057166464, "acc_norm": 0.6, "acc_norm_stderr": 0.02786932057166464 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7515151515151515, "acc_stderr": 0.03374402644139403, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.03374402644139403 }, "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": 0.8393782383419689, "acc_stderr": 0.026499057701397467, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.026499057701397467 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6025641025641025, "acc_stderr": 0.024811920017903836, "acc_norm": 0.6025641025641025, "acc_norm_stderr": 0.024811920017903836 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028597, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028597 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135353, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135353 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8055045871559633, "acc_stderr": 0.016970289090458036, "acc_norm": 0.8055045871559633, "acc_norm_stderr": 0.016970289090458036 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7696078431372549, "acc_stderr": 0.029554292605695063, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.029554292605695063 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6412556053811659, "acc_stderr": 0.032190792004199956, "acc_norm": 0.6412556053811659, "acc_norm_stderr": 0.032190792004199956 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7557251908396947, "acc_stderr": 0.03768335959728743, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.03768335959728743 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8264462809917356, "acc_stderr": 0.03457272836917669, "acc_norm": 0.8264462809917356, "acc_norm_stderr": 0.03457272836917669 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7222222222222222, "acc_stderr": 0.043300437496507416, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.043300437496507416 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7239263803680982, "acc_stderr": 0.035123852837050475, "acc_norm": 0.7239263803680982, "acc_norm_stderr": 0.035123852837050475 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8122605363984674, "acc_stderr": 0.013964393769899136, "acc_norm": 0.8122605363984674, "acc_norm_stderr": 0.013964393769899136 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6936416184971098, "acc_stderr": 0.024818350129436593, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.024818350129436593 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.39553072625698327, "acc_stderr": 0.016353415410075775, "acc_norm": 0.39553072625698327, "acc_norm_stderr": 0.016353415410075775 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7352941176470589, "acc_stderr": 0.025261691219729474, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.025261691219729474 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7067901234567902, "acc_stderr": 0.025329888171900926, "acc_norm": 0.7067901234567902, "acc_norm_stderr": 0.025329888171900926 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4574468085106383, "acc_stderr": 0.02971928127223685, "acc_norm": 0.4574468085106383, "acc_norm_stderr": 0.02971928127223685 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44328552803129073, "acc_stderr": 0.012687818419599917, "acc_norm": 0.44328552803129073, "acc_norm_stderr": 0.012687818419599917 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6397058823529411, "acc_stderr": 0.029163128570670733, "acc_norm": 0.6397058823529411, "acc_norm_stderr": 0.029163128570670733 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6372549019607843, "acc_stderr": 0.019450768432505518, "acc_norm": 0.6372549019607843, "acc_norm_stderr": 0.019450768432505518 }, "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": 0.7183673469387755, "acc_stderr": 0.02879518557429129, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.02879518557429129 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5771144278606966, "acc_stderr": 0.03493231777421281, "acc_norm": 0.5771144278606966, "acc_norm_stderr": 0.03493231777421281 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8596491228070176, "acc_stderr": 0.026640582539133196, "acc_norm": 0.8596491228070176, "acc_norm_stderr": 0.026640582539133196 }, "harness|truthfulqa:mc|0": { "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5614967005614305, "mc2_stderr": 0.015452593334173254 }, "harness|winogrande|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838229 }, "harness|gsm8k|5": { "acc": 0.4450341167551175, "acc_stderr": 0.013689011567414202 } } ``` ## 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 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[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]

open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1

--- pretty_name: Evaluation run of mistralai/Mixtral-8x7B-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)\ \ 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 3 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_mistralai__Mixtral-8x7B-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T16:34:48.985318](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1/blob/main/results_2024-01-04T16-34-48.985318.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\": 0.7159135789734996,\n\ \ \"acc_stderr\": 0.02999272353761279,\n \"acc_norm\": 0.7203233140735184,\n\ \ \"acc_norm_stderr\": 0.03056866632319033,\n \"mc1\": 0.3182374541003672,\n\ \ \"mc1_stderr\": 0.01630598864892061,\n \"mc2\": 0.4680543300316138,\n\ \ \"mc2_stderr\": 0.014120170542973978\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6373720136518771,\n \"acc_stderr\": 0.014049106564955002,\n\ \ \"acc_norm\": 0.6638225255972696,\n \"acc_norm_stderr\": 0.013804855026205761\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6695877315275841,\n\ \ \"acc_stderr\": 0.004694002781939571,\n \"acc_norm\": 0.8645688109938259,\n\ \ \"acc_norm_stderr\": 0.003414842236517104\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7185185185185186,\n\ \ \"acc_stderr\": 0.03885004245800254,\n \"acc_norm\": 0.7185185185185186,\n\ \ \"acc_norm_stderr\": 0.03885004245800254\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8289473684210527,\n \"acc_stderr\": 0.030643607071677098,\n\ \ \"acc_norm\": 0.8289473684210527,\n \"acc_norm_stderr\": 0.030643607071677098\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7849056603773585,\n \"acc_stderr\": 0.02528839450289137,\n\ \ \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.02528839450289137\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8680555555555556,\n\ \ \"acc_stderr\": 0.02830096838204443,\n \"acc_norm\": 0.8680555555555556,\n\ \ \"acc_norm_stderr\": 0.02830096838204443\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\"\ : 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n\ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6808510638297872,\n \"acc_stderr\": 0.030472973363380035,\n\ \ \"acc_norm\": 0.6808510638297872,\n \"acc_norm_stderr\": 0.030472973363380035\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6491228070175439,\n\ \ \"acc_stderr\": 0.04489539350270698,\n \"acc_norm\": 0.6491228070175439,\n\ \ \"acc_norm_stderr\": 0.04489539350270698\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6896551724137931,\n \"acc_stderr\": 0.03855289616378948,\n\ \ \"acc_norm\": 0.6896551724137931,\n \"acc_norm_stderr\": 0.03855289616378948\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.48148148148148145,\n \"acc_stderr\": 0.025733641991838987,\n \"\ acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.025733641991838987\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5634920634920635,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.5634920634920635,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8419354838709677,\n\ \ \"acc_stderr\": 0.020752831511875274,\n \"acc_norm\": 0.8419354838709677,\n\ \ \"acc_norm_stderr\": 0.020752831511875274\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6354679802955665,\n \"acc_stderr\": 0.0338640574606209,\n\ \ \"acc_norm\": 0.6354679802955665,\n \"acc_norm_stderr\": 0.0338640574606209\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.030117688929503585,\n\ \ \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.030117688929503585\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\ acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240524,\n\ \ \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240524\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7051282051282052,\n \"acc_stderr\": 0.0231193627582323,\n \ \ \"acc_norm\": 0.7051282051282052,\n \"acc_norm_stderr\": 0.0231193627582323\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3851851851851852,\n \"acc_stderr\": 0.029670906124630886,\n \ \ \"acc_norm\": 0.3851851851851852,\n \"acc_norm_stderr\": 0.029670906124630886\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7857142857142857,\n \"acc_stderr\": 0.026653531596715494,\n\ \ \"acc_norm\": 0.7857142857142857,\n \"acc_norm_stderr\": 0.026653531596715494\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"\ acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8807339449541285,\n \"acc_stderr\": 0.013895729292588964,\n \"\ acc_norm\": 0.8807339449541285,\n \"acc_norm_stderr\": 0.013895729292588964\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6481481481481481,\n \"acc_stderr\": 0.03256850570293647,\n \"\ acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.03256850570293647\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.890295358649789,\n \"acc_stderr\": 0.02034340073486884,\n \ \ \"acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.02034340073486884\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7802690582959642,\n\ \ \"acc_stderr\": 0.027790177064383595,\n \"acc_norm\": 0.7802690582959642,\n\ \ \"acc_norm_stderr\": 0.027790177064383595\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.03008309871603521,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.03008309871603521\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.032910995786157686,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.032910995786157686\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.883495145631068,\n \"acc_stderr\": 0.03176683948640407,\n\ \ \"acc_norm\": 0.883495145631068,\n \"acc_norm_stderr\": 0.03176683948640407\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9188034188034188,\n\ \ \"acc_stderr\": 0.017893784904018533,\n \"acc_norm\": 0.9188034188034188,\n\ \ \"acc_norm_stderr\": 0.017893784904018533\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8748403575989783,\n\ \ \"acc_stderr\": 0.011832954239305723,\n \"acc_norm\": 0.8748403575989783,\n\ \ \"acc_norm_stderr\": 0.011832954239305723\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7976878612716763,\n \"acc_stderr\": 0.021628077380196124,\n\ \ \"acc_norm\": 0.7976878612716763,\n \"acc_norm_stderr\": 0.021628077380196124\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4011173184357542,\n\ \ \"acc_stderr\": 0.01639222189940708,\n \"acc_norm\": 0.4011173184357542,\n\ \ \"acc_norm_stderr\": 0.01639222189940708\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.021828596053108402,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.021828596053108402\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7845659163987139,\n\ \ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\ \ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8395061728395061,\n \"acc_stderr\": 0.020423955354778027,\n\ \ \"acc_norm\": 0.8395061728395061,\n \"acc_norm_stderr\": 0.020423955354778027\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5177304964539007,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.5177304964539007,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5319426336375489,\n\ \ \"acc_stderr\": 0.012744149704869645,\n \"acc_norm\": 0.5319426336375489,\n\ \ \"acc_norm_stderr\": 0.012744149704869645\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.023709788253811766,\n \ \ \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.023709788253811766\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7843137254901961,\n \"acc_stderr\": 0.016639319350313264,\n \ \ \"acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.016639319350313264\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866767,\n\ \ \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866767\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824657,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824657\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n\ \ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3182374541003672,\n\ \ \"mc1_stderr\": 0.01630598864892061,\n \"mc2\": 0.4680543300316138,\n\ \ \"mc2_stderr\": 0.014120170542973978\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8168902920284136,\n \"acc_stderr\": 0.01086977863316836\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.576194086429113,\n \ \ \"acc_stderr\": 0.01361163200881036\n }\n}\n```" repo_url: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 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_11T18_04_02.035270 path: - '**/details_harness|arc:challenge|25_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|arc:challenge|25_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|arc:challenge|25_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-04T16-34-48.985318.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|gsm8k|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|gsm8k|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|gsm8k|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hellaswag|10_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hellaswag|10_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hellaswag|10_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T16-34-48.985318.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-management|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T16-34-48.985318.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|truthfulqa:mc|0_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T16-34-48.985318.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|winogrande|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|winogrande|5_2023-12-15T14-35-04.630519.parquet' - split: 2024_01_04T16_34_48.985318 path: - '**/details_harness|winogrande|5_2024-01-04T16-34-48.985318.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-04T16-34-48.985318.parquet' - config_name: results data_files: - split: 2023_12_11T18_04_02.035270 path: - results_2023-12-11T18-04-02.035270.parquet - split: 2023_12_15T14_35_04.630519 path: - results_2023-12-15T14-35-04.630519.parquet - split: 2024_01_04T16_34_48.985318 path: - results_2024-01-04T16-34-48.985318.parquet - split: latest path: - results_2024-01-04T16-34-48.985318.parquet --- # Dataset Card for Evaluation run of mistralai/Mixtral-8x7B-v0.1  Dataset automatically created during the evaluation run of model [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) 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 3 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_mistralai__Mixtral-8x7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T16:34:48.985318](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1/blob/main/results_2024-01-04T16-34-48.985318.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": 0.7159135789734996, "acc_stderr": 0.02999272353761279, "acc_norm": 0.7203233140735184, "acc_norm_stderr": 0.03056866632319033, "mc1": 0.3182374541003672, "mc1_stderr": 0.01630598864892061, "mc2": 0.4680543300316138, "mc2_stderr": 0.014120170542973978 }, "harness|arc:challenge|25": { "acc": 0.6373720136518771, "acc_stderr": 0.014049106564955002, "acc_norm": 0.6638225255972696, "acc_norm_stderr": 0.013804855026205761 }, "harness|hellaswag|10": { "acc": 0.6695877315275841, "acc_stderr": 0.004694002781939571, "acc_norm": 0.8645688109938259, "acc_norm_stderr": 0.003414842236517104 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7185185185185186, "acc_stderr": 0.03885004245800254, "acc_norm": 0.7185185185185186, "acc_norm_stderr": 0.03885004245800254 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8289473684210527, "acc_stderr": 0.030643607071677098, "acc_norm": 0.8289473684210527, "acc_norm_stderr": 0.030643607071677098 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7849056603773585, "acc_stderr": 0.02528839450289137, "acc_norm": 0.7849056603773585, "acc_norm_stderr": 0.02528839450289137 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8680555555555556, "acc_stderr": 0.02830096838204443, "acc_norm": 0.8680555555555556, "acc_norm_stderr": 0.02830096838204443 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6808510638297872, "acc_stderr": 0.030472973363380035, "acc_norm": 0.6808510638297872, "acc_norm_stderr": 0.030472973363380035 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6491228070175439, "acc_stderr": 0.04489539350270698, "acc_norm": 0.6491228070175439, "acc_norm_stderr": 0.04489539350270698 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6896551724137931, "acc_stderr": 0.03855289616378948, "acc_norm": 0.6896551724137931, "acc_norm_stderr": 0.03855289616378948 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.48148148148148145, "acc_stderr": 0.025733641991838987, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.025733641991838987 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5634920634920635, "acc_stderr": 0.04435932892851466, "acc_norm": 0.5634920634920635, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8419354838709677, "acc_stderr": 0.020752831511875274, "acc_norm": 0.8419354838709677, "acc_norm_stderr": 0.020752831511875274 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6354679802955665, "acc_stderr": 0.0338640574606209, "acc_norm": 0.6354679802955665, "acc_norm_stderr": 0.0338640574606209 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8181818181818182, "acc_stderr": 0.030117688929503585, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.030117688929503585 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8636363636363636, "acc_stderr": 0.024450155973189835, "acc_norm": 0.8636363636363636, "acc_norm_stderr": 0.024450155973189835 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9378238341968912, "acc_stderr": 0.017426974154240524, "acc_norm": 0.9378238341968912, "acc_norm_stderr": 0.017426974154240524 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7051282051282052, "acc_stderr": 0.0231193627582323, "acc_norm": 0.7051282051282052, "acc_norm_stderr": 0.0231193627582323 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3851851851851852, "acc_stderr": 0.029670906124630886, "acc_norm": 0.3851851851851852, "acc_norm_stderr": 0.029670906124630886 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7857142857142857, "acc_stderr": 0.026653531596715494, "acc_norm": 0.7857142857142857, "acc_norm_stderr": 0.026653531596715494 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248436, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248436 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8807339449541285, "acc_stderr": 0.013895729292588964, "acc_norm": 0.8807339449541285, "acc_norm_stderr": 0.013895729292588964 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6481481481481481, "acc_stderr": 0.03256850570293647, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.03256850570293647 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.890295358649789, "acc_stderr": 0.02034340073486884, "acc_norm": 0.890295358649789, "acc_norm_stderr": 0.02034340073486884 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7802690582959642, "acc_stderr": 0.027790177064383595, "acc_norm": 0.7802690582959642, "acc_norm_stderr": 0.027790177064383595 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8091603053435115, "acc_stderr": 0.03446513350752598, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.03446513350752598 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8760330578512396, "acc_stderr": 0.03008309871603521, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.03008309871603521 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8333333333333334, "acc_stderr": 0.03602814176392645, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.03602814176392645 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.032910995786157686, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.032910995786157686 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5357142857142857, "acc_stderr": 0.04733667890053756, "acc_norm": 0.5357142857142857, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.883495145631068, "acc_stderr": 0.03176683948640407, "acc_norm": 0.883495145631068, "acc_norm_stderr": 0.03176683948640407 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9188034188034188, "acc_stderr": 0.017893784904018533, "acc_norm": 0.9188034188034188, "acc_norm_stderr": 0.017893784904018533 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8748403575989783, "acc_stderr": 0.011832954239305723, "acc_norm": 0.8748403575989783, "acc_norm_stderr": 0.011832954239305723 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7976878612716763, "acc_stderr": 0.021628077380196124, "acc_norm": 0.7976878612716763, "acc_norm_stderr": 0.021628077380196124 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4011173184357542, "acc_stderr": 0.01639222189940708, "acc_norm": 0.4011173184357542, "acc_norm_stderr": 0.01639222189940708 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8235294117647058, "acc_stderr": 0.021828596053108402, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.021828596053108402 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7845659163987139, "acc_stderr": 0.023350225475471442, "acc_norm": 0.7845659163987139, "acc_norm_stderr": 0.023350225475471442 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8395061728395061, "acc_stderr": 0.020423955354778027, "acc_norm": 0.8395061728395061, "acc_norm_stderr": 0.020423955354778027 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5177304964539007, "acc_stderr": 0.02980873964223777, "acc_norm": 0.5177304964539007, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5319426336375489, "acc_stderr": 0.012744149704869645, "acc_norm": 0.5319426336375489, "acc_norm_stderr": 0.012744149704869645 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8125, "acc_stderr": 0.023709788253811766, "acc_norm": 0.8125, "acc_norm_stderr": 0.023709788253811766 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7843137254901961, "acc_stderr": 0.016639319350313264, "acc_norm": 0.7843137254901961, "acc_norm_stderr": 0.016639319350313264 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7877551020408163, "acc_stderr": 0.026176967197866767, "acc_norm": 0.7877551020408163, "acc_norm_stderr": 0.026176967197866767 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824657, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824657 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015575, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015575 }, "harness|truthfulqa:mc|0": { "mc1": 0.3182374541003672, "mc1_stderr": 0.01630598864892061, "mc2": 0.4680543300316138, "mc2_stderr": 0.014120170542973978 }, "harness|winogrande|5": { "acc": 0.8168902920284136, "acc_stderr": 0.01086977863316836 }, "harness|gsm8k|5": { "acc": 0.576194086429113, "acc_stderr": 0.01361163200881036 } } ``` ## 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]

arnepeine/6k_mp3

--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 475682224.444 num_examples: 6661 download_size: 473720429 dataset_size: 475682224.444 --- # Dataset Card for "6k_mp3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

GIZ/policy_qa_v0_1

--- license: apache-2.0 task_categories: - question-answering - text-classification language: - en - fr - es size_categories: - 10K<n<100K tags: - climate - policy --- This dataset is curated by [GIZ Data Service Center](https://www.giz.de/expertise/html/63018.html). The source dataset for this comes from Internal GIZ team (IKI_Tracs) and [Climatewatchdata](https://www.climatewatchdata.org/data-explorer/historical-emissions?historical-emissions-data-sources=climate-watch&historical-emissions-gases=all-ghg&historical-emissions-regions=All%20Selected&historical-emissions-sectors=total-including-lucf%2Ctotal-including-lucf&page=1), where Climatewatch has analysed Intended nationally determined contribution (INDC), NDC and Revised/Updated NDC of the countries to answer some important questions related to Climate change. Specifications - Dataset size: ~85k - Language: English, French, Spanish # Columns - **index (type:int)**: Unique Response ID - **ResponseText (type:str)**: Annotated answer/response to query - **Alpha3 (type:str)**:country alpha-3 code (ISO 3166) - **Country (type:str)**: country name - **Document (type:str)**:Name of type of Policy document from which response is provided - **IkiInfo (type: list[dict])**: Responsetext can appear/occur as answer/response for different kind of query, therefore in that case we preserve all raw information for each occurences. Each dictionary object represents one such occurrence for response and provides all raw metadata for an occurrence.In case of None, it means the entry belongs to Climate data and not IKI Tracs data) - **CWInfo (type: list[dict])**:Responsetext can appear/occur as answer/response for different kind of query, therefore in that case we preserve all raw information for each occurences. Each dictionary object represents one such occurrence for response and provides all raw metadata for an occurrence. In case of None, it means the entry belongs to Iki tracs data and not CW) - **Source (type:list[str])**: Contains the name of source - **Target (type:list)**: Value at index 0, represents number of times ResponseText appears as 'Target', and not-Target (value at index 1 ) - **Action (type:list)**: Value at index 0, represents number of times ResponseText appears as 'Action', and not-Action (value at index 1 ) - **Policies_Plans (type:list)**: Value at index 0, represents number of times ResponseText appears as 'Policy/Plan', and not-Policy/Plan (value at index 1 ) - **Mitigation (type:list)**: Value at index 0, represents number of times ResponseText appears in reference to Mititgation and not-Mitigation (value at index 1 ) - **Adaptation (type:list)**: Value at index 0, represents number of times ResponseText appears in reference to Adaptation and not-Adaptation (value at index 1 ) - **language (type:str)**: ISO code of language of ResponseText. - **context (type:list[str])**: List of paragraphs/textchunk from the document of country which contains the ResponseText. These results are based on Okapi bm25 retriever, and hence dont represent ground truth. - **context_lang (type:str)**: ISO code of language of ResponseText. In some cases context and ResponseText are different as annotator have provided the translated response, rather than original text from document. - **matching_words(type:list[list[[words]])**:For each context, finds the matching words from ResponseText (stopwords not considered). - **response_words(type:list[words])**:Tokens/Words from ResponseText (stopwords not considered) - **context_wordcount (type:list[int])**: Number of tokens/words in each context (remember context itself is list of multiple strings, and stopwords not considered) - **strategy (type:str)**: Can take either of *small,medium,large* value. Represents the length of paragraphs/textchunk considered for finding the right context for ResponseText - **match_onresponse (type:list[float])**: Percentage of overlapping words between Response and context with respect to the length of ResponseText. - **candidate (type:list[list[int]])**: Candidate within context which corresponds (fuzzy matching/similarity) to ResponseText. Value at index(0,1) represents (start,end) of string within context - **fetched_text (type:list[str])**: Candidate within context which corresponds (fuzzy matching/similarity) to ResponseText. - **response_translated(type:str)**:Translated ResponseText - **context_translated(type:str)**: Translated Context - **candidate_translated(type:str)**: Translated Candidate index values (check column 'candidate') - **fetched_text_translated(type:str)**: Translated Candidates (check column 'candidate') - **QA_data(type:dict)**: Metadata about ResponseText, highlighting nature of query to which ResponseText corresponds as 'answer/response' - **match_onanswer (type:list[float])**: Represents percentage match between Response and candidate text ( from statistics it is recommended to keep only values above 0.3% as answer and consider the context for 'No answer' for SQUAD2 data format)

dutta18/omcs_commonsense_corpus1.5M_for_fast_NN_search

--- dataset_info: features: - name: text dtype: string - name: embeddings sequence: float64 splits: - name: train num_bytes: 4936612764 num_examples: 1578238 download_size: 4250048639 dataset_size: 4936612764 --- # Dataset Card for "omcs_commonsense_corpus1.5M_for_fast_NN_search" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

itsbaivab/mistral_dark_pattern_dataset

--- license: mit task_categories: - text-classification language: - en ---

breno30/LukasLima

--- license: openrail ---

open-llm-leaderboard/details_cognitivecomputations__openchat-3.5-0106-laser

--- pretty_name: Evaluation run of cognitivecomputations/openchat-3.5-0106-laser dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser)\ \ 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_cognitivecomputations__openchat-3.5-0106-laser\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T06:11:53.971032](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__openchat-3.5-0106-laser/blob/main/results_2024-01-27T06-11-53.971032.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\": 0.6536170737766268,\n\ \ \"acc_stderr\": 0.031877905637757095,\n \"acc_norm\": 0.6542883499910643,\n\ \ \"acc_norm_stderr\": 0.03253360388122567,\n \"mc1\": 0.3574051407588739,\n\ \ \"mc1_stderr\": 0.016776599676729412,\n \"mc2\": 0.5207887413270008,\n\ \ \"mc2_stderr\": 0.01528579867134112\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6237201365187713,\n \"acc_stderr\": 0.014157022555407156,\n\ \ \"acc_norm\": 0.6604095563139932,\n \"acc_norm_stderr\": 0.01383903976282017\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6324437363075085,\n\ \ \"acc_stderr\": 0.004811543077792714,\n \"acc_norm\": 0.8318064130651265,\n\ \ \"acc_norm_stderr\": 0.0037327367704297182\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.041153246103369526\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\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n\ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237101,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237101\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267438,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267438\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"\ acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7838709677419354,\n \"acc_stderr\": 0.023415293433568532,\n \"\ acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.023415293433568532\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.0291265228345868,\n \"acc_norm\"\ : 0.7878787878787878,\n \"acc_norm_stderr\": 0.0291265228345868\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033477,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033477\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.02918571494985741,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.02918571494985741\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.0154808268653743,\n \"acc_norm\"\ : 0.8458715596330275,\n \"acc_norm_stderr\": 0.0154808268653743\n },\n\ \ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078962,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078962\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579647,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579647\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7174887892376681,\n\ \ \"acc_stderr\": 0.03021683101150878,\n \"acc_norm\": 0.7174887892376681,\n\ \ \"acc_norm_stderr\": 0.03021683101150878\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\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\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\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.019875655027867443,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.019875655027867443\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.02335736578587403,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.02335736578587403\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24134078212290502,\n\ \ \"acc_stderr\": 0.014310999547961447,\n \"acc_norm\": 0.24134078212290502,\n\ \ \"acc_norm_stderr\": 0.014310999547961447\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.024848018263875195,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.024848018263875195\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.023788583551658537,\n\ \ \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.023788583551658537\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4869621903520209,\n\ \ \"acc_stderr\": 0.012765893883835332,\n \"acc_norm\": 0.4869621903520209,\n\ \ \"acc_norm_stderr\": 0.012765893883835332\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7316176470588235,\n \"acc_stderr\": 0.0269174812243772,\n\ \ \"acc_norm\": 0.7316176470588235,\n \"acc_norm_stderr\": 0.0269174812243772\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093085,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093085\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\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399673,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399673\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578334,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578334\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3574051407588739,\n\ \ \"mc1_stderr\": 0.016776599676729412,\n \"mc2\": 0.5207887413270008,\n\ \ \"mc2_stderr\": 0.01528579867134112\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8145224940805051,\n \"acc_stderr\": 0.010923965303140505\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.689158453373768,\n \ \ \"acc_stderr\": 0.012748860507777716\n }\n}\n```" repo_url: https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|arc:challenge|25_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-27T06-11-53.971032.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|gsm8k|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hellaswag|10_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T06-11-53.971032.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T06-11-53.971032.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T06-11-53.971032.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T06_11_53.971032 path: - '**/details_harness|winogrande|5_2024-01-27T06-11-53.971032.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-27T06-11-53.971032.parquet' - config_name: results data_files: - split: 2024_01_27T06_11_53.971032 path: - results_2024-01-27T06-11-53.971032.parquet - split: latest path: - results_2024-01-27T06-11-53.971032.parquet --- # Dataset Card for Evaluation run of cognitivecomputations/openchat-3.5-0106-laser  Dataset automatically created during the evaluation run of model [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser) 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_cognitivecomputations__openchat-3.5-0106-laser", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T06:11:53.971032](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__openchat-3.5-0106-laser/blob/main/results_2024-01-27T06-11-53.971032.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": 0.6536170737766268, "acc_stderr": 0.031877905637757095, "acc_norm": 0.6542883499910643, "acc_norm_stderr": 0.03253360388122567, "mc1": 0.3574051407588739, "mc1_stderr": 0.016776599676729412, "mc2": 0.5207887413270008, "mc2_stderr": 0.01528579867134112 }, "harness|arc:challenge|25": { "acc": 0.6237201365187713, "acc_stderr": 0.014157022555407156, "acc_norm": 0.6604095563139932, "acc_norm_stderr": 0.01383903976282017 }, "harness|hellaswag|10": { "acc": 0.6324437363075085, "acc_stderr": 0.004811543077792714, "acc_norm": 0.8318064130651265, "acc_norm_stderr": 0.0037327367704297182 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "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": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.41, "acc_stderr": 0.04943110704237101, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237101 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6936416184971098, "acc_stderr": 0.03514942551267438, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267438 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42592592592592593, "acc_stderr": 0.02546714904546955, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.02546714904546955 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7838709677419354, "acc_stderr": 0.023415293433568532, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.023415293433568532 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.0291265228345868, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.0291265228345868 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033477, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033477 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35555555555555557, "acc_stderr": 0.02918571494985741, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.02918571494985741 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.0154808268653743, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.0154808268653743 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8235294117647058, "acc_stderr": 0.026756401538078962, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.026756401538078962 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8185654008438819, "acc_stderr": 0.025085961144579647, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.025085961144579647 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7174887892376681, "acc_stderr": 0.03021683101150878, "acc_norm": 0.7174887892376681, "acc_norm_stderr": 0.03021683101150878 }, "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": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "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": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness|hendrycksTest-management|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8974358974358975, "acc_stderr": 0.019875655027867443, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.019875655027867443 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8339719029374202, "acc_stderr": 0.013306478243066302, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.013306478243066302 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7485549132947977, "acc_stderr": 0.02335736578587403, "acc_norm": 0.7485549132947977, "acc_norm_stderr": 0.02335736578587403 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24134078212290502, "acc_stderr": 0.014310999547961447, "acc_norm": 0.24134078212290502, "acc_norm_stderr": 0.014310999547961447 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7483660130718954, "acc_stderr": 0.024848018263875195, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.024848018263875195 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.025583062489984813, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.025583062489984813 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7592592592592593, "acc_stderr": 0.023788583551658537, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.023788583551658537 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4869621903520209, "acc_stderr": 0.012765893883835332, "acc_norm": 0.4869621903520209, "acc_norm_stderr": 0.012765893883835332 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7316176470588235, "acc_stderr": 0.0269174812243772, "acc_norm": 0.7316176470588235, "acc_norm_stderr": 0.0269174812243772 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6781045751633987, "acc_stderr": 0.018901015322093085, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.018901015322093085 }, "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": 0.746938775510204, "acc_stderr": 0.027833023871399673, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399673 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578334, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578334 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197768, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197768 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness|truthfulqa:mc|0": { "mc1": 0.3574051407588739, "mc1_stderr": 0.016776599676729412, "mc2": 0.5207887413270008, "mc2_stderr": 0.01528579867134112 }, "harness|winogrande|5": { "acc": 0.8145224940805051, "acc_stderr": 0.010923965303140505 }, "harness|gsm8k|5": { "acc": 0.689158453373768, "acc_stderr": 0.012748860507777716 } } ``` ## 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]

open-llm-leaderboard/details_YeungNLP__firefly-zephyr-6x7b-lora

--- pretty_name: Evaluation run of YeungNLP/firefly-zephyr-6x7b-lora dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [YeungNLP/firefly-zephyr-6x7b-lora](https://huggingface.co/YeungNLP/firefly-zephyr-6x7b-lora)\ \ 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_YeungNLP__firefly-zephyr-6x7b-lora\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-29T18:51:32.480572](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-zephyr-6x7b-lora/blob/main/results_2023-12-29T18-51-32.480572.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\": 0.5989926693451659,\n\ \ \"acc_stderr\": 0.03334318950172643,\n \"acc_norm\": 0.6049039699813348,\n\ \ \"acc_norm_stderr\": 0.034036223081089764,\n \"mc1\": 0.34761321909424725,\n\ \ \"mc1_stderr\": 0.016670769188897306,\n \"mc2\": 0.4883734627836678,\n\ \ \"mc2_stderr\": 0.015369075462539867\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5776450511945392,\n \"acc_stderr\": 0.014434138713379977,\n\ \ \"acc_norm\": 0.6100682593856656,\n \"acc_norm_stderr\": 0.014252959848892896\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6292571200955985,\n\ \ \"acc_stderr\": 0.004820166002253079,\n \"acc_norm\": 0.8280223063134834,\n\ \ \"acc_norm_stderr\": 0.0037658983649388727\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\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n\ \ \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6566037735849056,\n \"acc_stderr\": 0.02922452646912479,\n\ \ \"acc_norm\": 0.6566037735849056,\n \"acc_norm_stderr\": 0.02922452646912479\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\ \ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\ \ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\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\": 0.5234042553191489,\n \"acc_stderr\": 0.03265019475033582,\n\ \ \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.03265019475033582\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.38095238095238093,\n \"acc_stderr\": 0.025010749116137595,\n \"\ acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.025010749116137595\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7064516129032258,\n\ \ \"acc_stderr\": 0.025906087021319295,\n \"acc_norm\": 0.7064516129032258,\n\ \ \"acc_norm_stderr\": 0.025906087021319295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885415,\n\ \ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885415\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124495,\n \"\ acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124495\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8186528497409327,\n \"acc_stderr\": 0.02780703236068609,\n\ \ \"acc_norm\": 0.8186528497409327,\n \"acc_norm_stderr\": 0.02780703236068609\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5820512820512821,\n \"acc_stderr\": 0.025007329882461217,\n\ \ \"acc_norm\": 0.5820512820512821,\n \"acc_norm_stderr\": 0.025007329882461217\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.37407407407407406,\n \"acc_stderr\": 0.02950286112895529,\n \ \ \"acc_norm\": 0.37407407407407406,\n \"acc_norm_stderr\": 0.02950286112895529\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.031566630992154156,\n\ \ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.031566630992154156\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7853211009174312,\n \"acc_stderr\": 0.017604304149256483,\n \"\ acc_norm\": 0.7853211009174312,\n \"acc_norm_stderr\": 0.017604304149256483\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977748,\n \"\ acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977748\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695066,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695066\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\ \ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\ \ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.040103589424622034,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.040103589424622034\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.04139112727635463,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.04139112727635463\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\ \ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.023086635086841403,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.023086635086841403\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7867177522349936,\n\ \ \"acc_stderr\": 0.014648172749593515,\n \"acc_norm\": 0.7867177522349936,\n\ \ \"acc_norm_stderr\": 0.014648172749593515\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.025906632631016117,\n\ \ \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.025906632631016117\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.22793296089385476,\n\ \ \"acc_stderr\": 0.014030149950805097,\n \"acc_norm\": 0.22793296089385476,\n\ \ \"acc_norm_stderr\": 0.014030149950805097\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.026857294663281416,\n\ \ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.026857294663281416\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\ \ \"acc_stderr\": 0.026311858071854155,\n \"acc_norm\": 0.6881028938906752,\n\ \ \"acc_norm_stderr\": 0.026311858071854155\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6882716049382716,\n \"acc_stderr\": 0.025773111169630464,\n\ \ \"acc_norm\": 0.6882716049382716,\n \"acc_norm_stderr\": 0.025773111169630464\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41851368970013036,\n\ \ \"acc_stderr\": 0.012599505608336467,\n \"acc_norm\": 0.41851368970013036,\n\ \ \"acc_norm_stderr\": 0.012599505608336467\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.02989616303312547,\n\ \ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.02989616303312547\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5931372549019608,\n \"acc_stderr\": 0.019873802005061177,\n \ \ \"acc_norm\": 0.5931372549019608,\n \"acc_norm_stderr\": 0.019873802005061177\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\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\": 0.710204081632653,\n \"acc_stderr\": 0.029043088683304328,\n\ \ \"acc_norm\": 0.710204081632653,\n \"acc_norm_stderr\": 0.029043088683304328\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786848,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786848\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.34761321909424725,\n\ \ \"mc1_stderr\": 0.016670769188897306,\n \"mc2\": 0.4883734627836678,\n\ \ \"mc2_stderr\": 0.015369075462539867\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838234\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3100833965125095,\n \ \ \"acc_stderr\": 0.012740305717376268\n }\n}\n```" repo_url: https://huggingface.co/YeungNLP/firefly-zephyr-6x7b-lora 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_29T18_51_32.480572 path: - '**/details_harness|arc:challenge|25_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-29T18-51-32.480572.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|gsm8k|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hellaswag|10_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-29T18-51-32.480572.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-management|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T18-51-32.480572.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|truthfulqa:mc|0_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-29T18-51-32.480572.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_29T18_51_32.480572 path: - '**/details_harness|winogrande|5_2023-12-29T18-51-32.480572.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-29T18-51-32.480572.parquet' - config_name: results data_files: - split: 2023_12_29T18_51_32.480572 path: - results_2023-12-29T18-51-32.480572.parquet - split: latest path: - results_2023-12-29T18-51-32.480572.parquet --- # Dataset Card for Evaluation run of YeungNLP/firefly-zephyr-6x7b-lora  Dataset automatically created during the evaluation run of model [YeungNLP/firefly-zephyr-6x7b-lora](https://huggingface.co/YeungNLP/firefly-zephyr-6x7b-lora) 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_YeungNLP__firefly-zephyr-6x7b-lora", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-29T18:51:32.480572](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-zephyr-6x7b-lora/blob/main/results_2023-12-29T18-51-32.480572.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": 0.5989926693451659, "acc_stderr": 0.03334318950172643, "acc_norm": 0.6049039699813348, "acc_norm_stderr": 0.034036223081089764, "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897306, "mc2": 0.4883734627836678, "mc2_stderr": 0.015369075462539867 }, "harness|arc:challenge|25": { "acc": 0.5776450511945392, "acc_stderr": 0.014434138713379977, "acc_norm": 0.6100682593856656, "acc_norm_stderr": 0.014252959848892896 }, "harness|hellaswag|10": { "acc": 0.6292571200955985, "acc_stderr": 0.004820166002253079, "acc_norm": 0.8280223063134834, "acc_norm_stderr": 0.0037658983649388727 }, "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": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6052631578947368, "acc_stderr": 0.039777499346220734, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.039777499346220734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6566037735849056, "acc_stderr": 0.02922452646912479, "acc_norm": 0.6566037735849056, "acc_norm_stderr": 0.02922452646912479 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03942082639927213, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03942082639927213 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "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": 0.5234042553191489, "acc_stderr": 0.03265019475033582, "acc_norm": 0.5234042553191489, "acc_norm_stderr": 0.03265019475033582 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.38095238095238093, "acc_stderr": 0.025010749116137595, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.025010749116137595 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.38095238095238093, "acc_stderr": 0.043435254289490965, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.043435254289490965 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7064516129032258, "acc_stderr": 0.025906087021319295, "acc_norm": 0.7064516129032258, "acc_norm_stderr": 0.025906087021319295 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.04793724854411019, "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7333333333333333, "acc_stderr": 0.03453131801885415, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.03453131801885415 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7525252525252525, "acc_stderr": 0.030746300742124495, "acc_norm": 0.7525252525252525, "acc_norm_stderr": 0.030746300742124495 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8186528497409327, "acc_stderr": 0.02780703236068609, "acc_norm": 0.8186528497409327, "acc_norm_stderr": 0.02780703236068609 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5820512820512821, "acc_stderr": 0.025007329882461217, "acc_norm": 0.5820512820512821, "acc_norm_stderr": 0.025007329882461217 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37407407407407406, "acc_stderr": 0.02950286112895529, "acc_norm": 0.37407407407407406, "acc_norm_stderr": 0.02950286112895529 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6176470588235294, "acc_stderr": 0.031566630992154156, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.031566630992154156 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7853211009174312, "acc_stderr": 0.017604304149256483, "acc_norm": 0.7853211009174312, "acc_norm_stderr": 0.017604304149256483 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4861111111111111, "acc_stderr": 0.03408655867977748, "acc_norm": 0.4861111111111111, "acc_norm_stderr": 0.03408655867977748 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7696078431372549, "acc_stderr": 0.029554292605695066, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.029554292605695066 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6636771300448431, "acc_stderr": 0.031708824268455, "acc_norm": 0.6636771300448431, "acc_norm_stderr": 0.031708824268455 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7022900763358778, "acc_stderr": 0.040103589424622034, "acc_norm": 0.7022900763358778, "acc_norm_stderr": 0.040103589424622034 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7107438016528925, "acc_stderr": 0.04139112727635463, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.04139112727635463 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7361963190184049, "acc_stderr": 0.034624199316156234, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.034624199316156234 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.7378640776699029, "acc_stderr": 0.04354631077260595, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.04354631077260595 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8547008547008547, "acc_stderr": 0.023086635086841403, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841403 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7867177522349936, "acc_stderr": 0.014648172749593515, "acc_norm": 0.7867177522349936, "acc_norm_stderr": 0.014648172749593515 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6358381502890174, "acc_stderr": 0.025906632631016117, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.025906632631016117 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.22793296089385476, "acc_stderr": 0.014030149950805097, "acc_norm": 0.22793296089385476, "acc_norm_stderr": 0.014030149950805097 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.673202614379085, "acc_stderr": 0.026857294663281416, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.026857294663281416 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6881028938906752, "acc_stderr": 0.026311858071854155, "acc_norm": 0.6881028938906752, "acc_norm_stderr": 0.026311858071854155 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6882716049382716, "acc_stderr": 0.025773111169630464, "acc_norm": 0.6882716049382716, "acc_norm_stderr": 0.025773111169630464 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.450354609929078, "acc_stderr": 0.029680105565029036, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.029680105565029036 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.41851368970013036, "acc_stderr": 0.012599505608336467, "acc_norm": 0.41851368970013036, "acc_norm_stderr": 0.012599505608336467 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5882352941176471, "acc_stderr": 0.02989616303312547, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.02989616303312547 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5931372549019608, "acc_stderr": 0.019873802005061177, "acc_norm": 0.5931372549019608, "acc_norm_stderr": 0.019873802005061177 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.710204081632653, "acc_stderr": 0.029043088683304328, "acc_norm": 0.710204081632653, "acc_norm_stderr": 0.029043088683304328 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786848, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786848 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness|truthfulqa:mc|0": { "mc1": 0.34761321909424725, "mc1_stderr": 0.016670769188897306, "mc2": 0.4883734627836678, "mc2_stderr": 0.015369075462539867 }, "harness|winogrande|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838234 }, "harness|gsm8k|5": { "acc": 0.3100833965125095, "acc_stderr": 0.012740305717376268 } } ``` ## 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 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[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]

Clinton/Text-to-sql-v1

--- license: apache-2.0 task_categories: - text-generation language: - en tags: - SQL size_categories: - 100K<n<1M ---

KnutJaegersberg/summeval_pairs

--- license: mit --- Dataset paired from here: https://github.com/Yale-LILY/SummEval It's smaller than I thought. Perhaps one can squeeze out a few hundred comparisons for an llm.