davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
uname-n/slim-orca-dedup-chat	--- dataset_info: features: - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 602329260 num_examples: 363491 download_size: 301939359 dataset_size: 602329260 configs: - config_name: default data_files: - split: train path: data/train-* ---
bhjhk/masmamad8	--- license: creativeml-openrail-m ---
AdapterOcean/med_alpaca_standardized_cluster_86	--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 51349063 num_examples: 5266 download_size: 15035670 dataset_size: 51349063 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_86" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DynamicSuperb/SpeechDetection_LJSpeech	--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 58016282.72519084 num_examples: 200 download_size: 56990484 dataset_size: 58016282.72519084 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "speechDetection_LJSpeech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
myrtotsok/clf-3	--- dataset_info: features: - name: request dtype: string - name: label dtype: string splits: - name: train num_bytes: 121051 num_examples: 1120 - name: validation num_bytes: 30256 num_examples: 280 download_size: 28195 dataset_size: 151307 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
FINNUMBER/FINCH_TRAIN_ALL_900_per100_NEW_Rationale	--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: sub_task dtype: string - name: rationale dtype: string - name: correct dtype: bool - name: check dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 3764071 num_examples: 900 download_size: 2070353 dataset_size: 3764071 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/stg44_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of stg44/StG44/StG44 (Girls' Frontline) This is the dataset of stg44/StG44/StG44 (Girls' Frontline), containing 114 images and their tags. The core tags of this character are `blonde_hair, long_hair, green_eyes, hat, bangs, military_hat, black_headwear, 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 \| 114 \| 158.86 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 114 \| 82.74 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 293 \| 187.08 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 114 \| 136.94 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 293 \| 270.17 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/stg44_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, black_necktie, military_uniform, solo, looking_at_viewer, white_gloves, white_shirt, black_jacket, smile, upper_body, simple_background, white_background, long_sleeves, closed_mouth, open_mouth \| \| 1 \| 15 \| ![](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_necktie, black_skirt, looking_at_viewer, military_uniform, solo, white_shirt, assault_rifle, white_gloves, black_jacket, closed_mouth, holding_gun, black_thighhighs, boots, collared_shirt, black_footwear, military_jacket, belt, long_sleeves, smile, full_body, peaked_cap, simple_background, standing \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, black_pantyhose, black_skirt, eyewear_on_head, high_heels, solo, sunglasses, white_shirt, black_footwear, full_body, looking_at_viewer, medium_breasts, pencil_skirt, black_choker, black_jacket, holding, legs, torn_pantyhose, alternate_costume, black_coat, blush, jacket_on_shoulders, long_sleeves, office_lady, shoes, sitting, standing_on_one_leg, tinted_eyewear \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_necktie \| military_uniform \| solo \| looking_at_viewer \| white_gloves \| white_shirt \| black_jacket \| smile \| upper_body \| simple_background \| white_background \| long_sleeves \| closed_mouth \| open_mouth \| black_skirt \| assault_rifle \| holding_gun \| black_thighhighs \| boots \| collared_shirt \| black_footwear \| military_jacket \| belt \| full_body \| peaked_cap \| standing \| black_pantyhose \| eyewear_on_head \| high_heels \| sunglasses \| medium_breasts \| pencil_skirt \| black_choker \| holding \| legs \| torn_pantyhose \| alternate_costume \| black_coat \| blush \| jacket_on_shoulders \| office_lady \| shoes \| sitting \| standing_on_one_leg \| tinted_eyewear \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:----------------\|:-------------------\|:-------\|:--------------------\|:---------------\|:--------------\|:---------------\|:--------\|:-------------\|:--------------------\|:-------------------\|:---------------\|:---------------\|:-------------\|:--------------\|:----------------\|:--------------\|:-------------------\|:--------\|:-----------------\|:-----------------\|:------------------\|:-------\|:------------\|:-------------\|:-----------\|:------------------\|:------------------\|:-------------\|:-------------\|:-----------------\|:---------------\|:---------------\|:----------\|:-------\|:-----------------\|:--------------------\|:-------------\|:--------\|:----------------------\|:--------------\|:--------\|:----------\|:----------------------\|:-----------------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 15 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](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 \| X \| X \| X \| X \|
huggingartists/bob-dylan	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/bob-dylan" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 2.91167 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/22306423b6ad8777d1ed5b33ad8b0d0b.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/bob-dylan"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Bob Dylan</div> <a href="https://genius.com/artists/bob-dylan"> <div style="text-align: center; font-size: 14px;">@bob-dylan</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/bob-dylan). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/bob-dylan") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|2241\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/bob-dylan") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
AdapterOcean/med_alpaca_standardized_cluster_30_std	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 20960368 num_examples: 32918 download_size: 10266852 dataset_size: 20960368 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_30_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nlp-with-deeplearning/ko.SHP	--- license: cc-by-nc-sa-4.0 task_categories: - text-generation - question-answering tags: - human feedback - rlhf - preferences - reddit - preference model - RL - NLG - evaluation size_categories: - 100K<n<1M language: - ko - en --- # 🚢 Korean Stanford Human Preferences Dataset (Ko.SHP) 이 데이터셋은 자체 구축한 번역기를 활용하여 [stanfordnlp/SHP](https://huggingface.co/datasets/stanfordnlp/SHP) 데이터셋을 번역한 것입니다. 아래의 내용은 해당 번역기로 README 파일을 번역한 것입니다. 참고 부탁드립니다. If you mention this dataset in a paper, please cite the paper: [Understanding Dataset Difficulty with V-Usable Information (ICML 2022)](https://proceedings.mlr.press/v162/ethayarajh22a.html). ## Summary SHP는 요리에서 법률 조언에 이르기까지 18가지 다른 주제 영역의 질문/지침에 대한 응답에 대한 385K 집단 인간 선호도 데이터 세트이다. 기본 설정은 다른 응답에 대 한 한 응답의 유용성을 반영 하기 위한 것이며 RLHF 보상 모델 및 NLG 평가 모델 (예: [SteamSHP](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl))을 훈련 하는 데 사용 하도록 설정 됩니다. 각각의 예는 질문/지시 및 그 게시물에 대한 한 쌍의 최상위 코멘트를 갖는 레딧 게시물이며, 여기서 하나의 코멘트는 (종합적으로) 레딧 사용자에 의해 더 선호된다. SHP는 댓글 A가 댓글 B 뒤에 작성되었지만 그럼에도 불구하고 점수가 더 높으면 표면적으로는 A가 B보다 더 선호된다는 사실을 이용한다. A가 B보다 먼저 작성되었으면 더 높은 점수가 더 많은 가시성의 결과일 수 있기 때문에 결론을 내릴 수 없었다. 우리는 선호도 라벨이 어떤 반응이 덜 유해하기보다는 더 도움이 되는지를 반영하기 위한 데이터를 선택했으며 후자는 많은 과거 작업의 초점이다. SHP는 [Anthropic의 HH-RLHF 데이터 세트](https://huggingface.co/datasets/Anthropic/hh-rlhf)와 어떻게 다른가요? 특히, SHP의 모든 데이터는 자연적으로 발생하고 인간이 작성하는 반면 HH-RLHF의 응답은 기계 작성되어 서로를 보완할 수 있는 두 가지 매우 다른 분포를 제공한다. \| Dataset \| Size \| Input \| Label \| Domains \| Data Format \| Length \| \| -------------------- \| ---- \| -------------------------- \| ---------------------------- \| ------------------------- \| ------------------------------------- \| --------------- \| \| SHP \| 385K \| 자연 발생 인간 작성 응답 \| Collective Human Preference \| 18 (labelled) \| Question/Instruction + Response (Single-turn) \| 최대 10.1K T5 토큰 \| \| HH-RLHF \| 91K \| LLM과의 대화 \| 개별 인간 선호도 \| 레이블이 지정되지 않음 \| Live Chat (Multi-turn) \| 최대 1.5K T5 토큰 \| SHP는 [ELI5](https://huggingface.co/datasets/eli5#source-data)와 같이 Reddit을 스크래핑한 다른 데이터 세트와 어떻게 다른가요? SHP는 타임스탬프 정보를 사용 하 여 선호도를 유추 하는 반면 ELI5는 주석 및 점수만 제공 합니다. 후자는 이전에 만든 주석이 더 많은 가시성에서 더 높은 점수를 얻는 경향이 있기 때문에 선호도를 유추 하기에 충분하지 않습니다. 또한 더 많은 도메인의 데이터를 포함합니다. \| Dataset \| Size \| Comments + Scores \| Preferences \| Number of Domains \| \| -------------------- \| ---- \| ------------------ \| -------------\| ------------------ \| \| SHP \| 385K \| Yes \| Yes \| 18 \| \| ELI5 \| 270K \| Yes \| No \| 3 \| ## 데이터 구조 각 하위 레딧에 대해 하나씩 18개의 디렉터리가 있으며 각 디렉터리에는 학습, 유효성 검사 및 테스트 데이터를 위한 JSONL 파일이 포함되어 있습니다. Huggingface의 `datasets` 라이브러리를 사용하여 데이터를 가져오는 방법은 다음과 같습니다. ```python from datasets import load_dataset # Load all the data dataset = load_dataset("stanfordnlp/shp") # Load one of the subreddits dataset = load_dataset("stanfordnlp/shp", data_dir="askculinary") ``` 다음은 `askculinary/train.json`의 예입니다. ``` { `post_id`:"qt3nxl", `domain`:"askculinary_train", `upvote_ratio`:0.98, `history`:"라즈베리를 분해하는 가장 좋은 방법은 무엇입니까? 이와 같이, 그러나 개별 씨앗까지: https:\/\/i.imgur.com\/Z0c6ZKE.jpg 핀셋으로 분리해 왔는데 시간이 많이 걸립니다. 이번 주말까지 약 10파운드가 소요됩니다. `c_root_id_A`:"hkh25sc", `c_root_id_B`:"hkh25lp", `created_at_utc_A`:1636822112, `created_at_utc_B`:1636822110, `score_A`:340, `score_B`:166, `human_ref_A`:"Pectinex, 아마도? 셀룰로오스를 분해하는 효소입니다. citrus를 사용하면 pectinex의 묽은 용액에 밤새 앉아 결합 조직을 분해하게 됩니다. 결국 완벽한 citrus supremes가 됩니다. 라즈베리를 더 짧은 시간 동안 앉게 되면 같은 방식으로 종자를 분리할 수 있는지 궁금합니다. 여기 예제가 있습니다. https:\/\/www.chefsteps.com\/activities\/perfect-citrus-supreme", `human_ref_B`:"라즈베리 주스는 처음에는 밝은 얼룩을 만들지만 몇 주 후면 거의 아무것도 사라지기 시작합니다. 천연 염료 세계에서 탈주 염료로 알려진 것은 세탁이나 빛에 노출되지 않아도 사라질 것입니다. 그녀가 드레스에 이 얼룩의 멋진 사진을 많이 얻기를 바랍니다. 곧 그것이 그녀가 남긴 전부이기 때문입니다." `labels`:1, `seconds_difference`:2.0, `score_ratio`:2.0481927711 } ``` 상기 필드들은: - ```post_id```: the ID of the Reddit post (string) - ```domain```: subreddit and split the example is drawn from, separated by underscore (string) - ```upvote_ratio```: 긍정 (일명 upvotes) (float) 게시물에서 받은 투표 비율입니다. - ```history```: Post title concatented to post body (string) - ```c_root_id_A```: comment A의 ID (string) - ```c_root_id_B```: comment B (string)의 ID - ```created_at_utc_A```: utc timestamp of when comment A is created (integer) - ```created_at_utc_B```: utc timestamp of when comment B is created (integer) - ```score_A```: (# positive votes - # negative votes + 1) received by comment A (integer) - ```score_B```: (# positive votes - # negative votes + 1) received by comment B (integer) - ```human_ref_A```: comment A의 텍스트 (string) - ```human_ref_B```: comment B의 텍스트 (string) - ```labels```: 선호도 레이블 -- A가 B보다 선호되는 경우 1이고, B가 A보다 선호되는 경우 0입니다. 레이블 분포가 대략 50/50이 되도록 무작위화되었습니다. (정수) - ```seconds_difference```: 덜 선호되는 코멘트가 생성된 후 몇 초 후에 더 선호되는 코멘트가 생성되었는지(항상 >= 0일 것임) (정수) - ```score_ratio```: 더 선호하는 댓글의 점수와 덜 선호하는 댓글의 점수의 비율 (>= 1) (float) ## Dataset Design ### 도메인 선택 데이터는 서브레딧 이라는 토픽별 포라로 구성된 공개 포럼인 Reddit에서 공급됩니다. 예를 들어 `askculinary` 하위 레딧은 사용자가 요리 관련 질문을 하고 다른 사용자에 의해 응답 되는 것입니다. SHP에는 18개의 다른 하위 레딧에서 긁어낸 주석에 대한 열차, 유효성 검사 및 테스트 분할이 포함되어 있습니다. 우리는 다음을 기반으로 하위 레딧을 선택했습니다. 1. 잘 알려진 것인지 여부(가입자수 >= 100K) 2. 게시물이 질문 또는 지시를 내릴 것으로 예상되었는지 여부 3. 응답이 얼마나 도움이 되는지에 따라 평가되는지 여부 4. 코멘트가 전적으로 개인 경험에 대한 것이 아니라 일부 객관성에 뿌리를 두어야 하는지 여부(예: `askscience` 대 `AskAmericans`)입니다. 열차/검증/테스트 분할은 하위 레딧의 포스트 ID를 각각 90%/5%/5% 비율로 분할하여 생성되어 여러 분할에 포스트가 나타나지 않는다. 상이한 게시물들은 상이한 수의 코멘트들을 갖기 때문에, 각각의 분할에서의 선호들의 수는 정확히 90%/5%/5%가 아니다: \| subreddit \| train \| validation \| test \| total \| \| ------------------ \| -------: \| ---------: \| ---: \| ----: \| \| askacademia \| 31450 \| 2095 \| 1708 \| 35253 \| \| askanthropology \| 3910 \| 203 \| 268 \| 4381 \| \| askbaking \| 44007 \| 2096 \| 1544 \| 47647 \| \| askcarguys \| 3227 \| 159 \| 117 \| 3503 \| \| askculinary \| 45710 \| 2094 \| 2563 \| 50367 \| \| askdocs \| 6449 \| 315 \| 455 \| 7219 \| \| askengineers \| 57096 \| 3154 \| 2638 \| 62888 \| \| askhistorians \| 3264 \| 113 \| 164 \| 3541 \| \| askhr \| 8295 \| 641 \| 395 \| 9331 \| \| askphilosophy \| 10307 \| 608 \| 677 \| 11592 \| \| askphysics \| 7364 \| 409 \| 587 \| 8360 \| \| askscience \| 13316 \| 899 \| 977 \| 15192 \| \| asksciencefiction \| 29382 \| 1576 \| 1987 \| 32945 \| \| asksocialscience \| 2706 \| 147 \| 188 \| 3041 \| \| askvet \| 3300 \| 170 \| 224 \| 3694 \| \| changemyview \| 38173 \| 1637 \| 1836 \| 41646 \| \| explainlikeimfive \| 19592 \| 1014 \| 1070 \| 21676 \| \| legaladvice \| 21170 \| 1106 \| 1011 \| 23287 \| \| ALL \| 348718 \| 18436 \| 18409 \| 385563 \| ### 데이터 선택 포스트/댓글의 점수는 1에 사용자로부터의 상향 투표 수(승인)를 곱하고 하향 투표 수(승인 취소)를 뺀 값입니다. 점수의 값은 상대적입니다. 트래픽이 많은 하위 레딧(게시물)에서는 점수가 높은 게시물(댓글)이 더 많습니다. 게시물에서 더 일찍 게시된 댓글은 단순히 노출이 많아 점수가 더 높은 경향이 있을 것이므로 선호도를 추론할 때 타임스탬프 정보를 사용하는 것이 필수적이다. 게시물 P와 두 개의 주석(A,B)이 주어지면 데이터 세트에 선호도 A > B만 포함했다. 1. A는 늦지 않게 B로 작성되었고 A는 B보다 높은 점수를 갖는다. 2. 게시물은 2023년 이전에 만들어진 셀프-포스트(즉, 텍스트의 본문이고 다른 페이지로의 링크가 아님)이며, 편집되지 않았으며, NSFW(18 초과)가 아니다. 3. 삭제된 사용자, 사회자 또는 게시물 작성자에 의해 어떠한 코멘트도 이루어지지 않았다. 게시물은 삭제된 사용자 또는 진행자가 만들지 않았습니다. 4. 게시물은 점수가 >=10이고 각 코멘트는 점수가 >=2(적어도 한 번 이상 투표)이다. 주석이 있는 게시물은 `n` 데이터에서 최대 (`n` `2`) 환경 설정을 선택할 수 있습니다. 게시물당 댓글 수는 파레토 배포이기 때문에 상대적으로 적은 수의 게시물이 데이터를 지배하는 것을 방지하기 위해 게시물당 50개의 댓글으로 스크래핑을 제한했다. 이는 위의 모든 기준을 충족해야 하기 때문에 실제로는 훨씬 적은 수이지만 각 게시물에 데이터 집합에서 최대 (`50` `2`를 선택) 주석이 있을 수 있음을 의미 합니다. 레드딧은 서브레드딧마다 상위 1000개 이상의 게시물을 얻는 것을 매우 어렵게 만든다. 최상위 1,000개의 게시물부터 시작하여 Reddit의 검색 기능을 사용하여 각 게시물과 가장 유사한 25개의 게시물을 검색하여 하위 레딧당 최대 7500개의 고유한 게시물 ID를 얻었다. ### 전처리 전처리를 최소한으로 유지하려고 노력했습니다. 서브레디트-특정 약어는 확장되었다(예를 들어, "CMV"를 "내 견해를 변경"으로). 하이퍼링크에서, 참조 텍스트만이 유지되고 URL이 제거되었다(URL이 기입된 경우, 그것은 유지되었다). ## 기본 설정 모델 만들기 ### Finetuning 인간 선호도(예를 들어, NLG 평가 또는 RLHF 보상 모델에 대해)를 예측하기 위해 모델을 피니튜닝하고자 하는 경우, 여기 몇 가지 유용한 팁이 있다: 1. *데이터를 전처리합니다. * 총 입력 길이는 모델의 토큰 제한 (일반적으로 512 토큰)에 적합 해야 합니다. FLAN-T5와 같은 모델은 위치 임베딩을 사용하지만 512개 토큰 이상의 입력에서 손실을 조정하면 손실이 수렴하지 않는다는 것을 발견했다. 이를 방지 하려면 게시글 텍스트 (`history` 필드에서)를 가능한 한 잘라서 전체 입력이 512 토큰 아래에 있도록 합니다 (그러나 주석을 잘리지 않음). 여전히 512 토큰 이상이면 예제를 건너뜁니다. 2. 충분히 큰 모델을 사용 합니다. 모든 트레이닝 데이터에 걸쳐 단일 FLAN-T5-xl 모델을 피니튜닝하는 것은 72-73%(전체 입력이 토큰 한계 내에 맞는 예시의 모든 도메인에 걸쳐) 사이의 테스트 정확도를 제공해야 하며, 개별 서브레딧의 경우 65-80% 범위이다. 3. *도메인 내 예측을 수행 합니다. * 하위 레딧이 관련이 없는 경우 도메인 외 성능이 좋지 않습니다 (예: 환경 설정을 미세 조정 하 고 환경 설정을 테스트 하는 경우 `askculinary` `askcarguys`). 4. 더 적은 에폭에 대해 훈련 InstructGPT 종이 페이퍼는 1 에폭에 대해서만 보상 모델을 훈련하는 것을 제안합니다. 동일한 코멘트가 여러 선호도에서 나타나기 때문에 데이터에 과적합되기 쉽다. 5. *더 적은 데이터에 대한 교육이 도움이 될 수 있습니다 . 큰 `score_ratio`를 사용하는 환경 설정(예: 주석 B의 점수가 2배인 주석 A)은 모델을 조정하기 위한 더 강력한 신호를 제공하므로 특정 이상의 환경 설정만 고려하려는 것일 수 있습니다 `score_ratio`. 게시물당 선호도 수는 Pareto-distributed이므로 모델이 특정 게시물에 과도하게 적합 하는 것을 방지 하기 위해 특정 게시물에서 선호도 수를 제한 하는 것이 좋습니다. ### 평가 약한 기본 설정보다 강력한 기본 설정을 예측하는 것이 더 쉽기 때문에 단일 정확도 값을 보고하는 대신 성능 곡선을 `score_ratio`의 함수로 보고하는 것이 좋습니다. 예를 들어, 여기 위의 제안들을 사용하여 질문적 데이터에 대해 트레이닝된 FLAN-T5-xl 모델에 대한 정확도 곡선이 있다. 주황색 라인은 2+ 스코어 비율을 갖는 선호도에만 피니튜닝하고 과적합을 방지하기 위해 각 포스트로부터 5개 이하의 선호도를 사용하는 것이다: 로 이루어진 군에서 선택되는 어느 하나인 것을 특징으로 하는 유기 발광 표시 장치. [그래프](curve.png) 우리는 더 낮지만 더 높은 품질의 데이터를 미세 조정하는 것이 실제 단점이 없는 점수 비율이 3.5 미만인 테스트 데이터에 대한 더 높은 정확도로 이어진다는 것을 알 수 있다! 토큰 제한 내에 입력이 맞지 않는 예는 모델에서 처리할 것으로 예상할 수 없기 때문에 실험에서 제외되었다. ### SteamSHP - Open-Source Preference Model 우리는 SHP 데이터 세트와 Anthropic의 HH-RLHF의 도움 데이터 모두에 대해 두 개의 FLAN-T5 모델을 미세 조정했다. 그들은 - 테스트 데이터에서 72.8%를 달성하는 3B 매개 변수 모델인 [SteamSHP-XL](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl)입니다. - 테스트 데이터에서 72.0%를 달성하는 780M 매개 변수 모델인 [SteamSHP-Large](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-large)입니다. NLG 평가, RLHF에 대한 보상 모델 구축 또는 적합하다고 생각하는 다른 목적으로 스팀SHP를 사용하는 것이 좋습니다! ## 편향 및 제한 사항 ### Biases NSFW(18세 이상) 콘텐츠로 게시물을 걸러내고, 잘 조정되고 괴롭힘과 편협에 대한 정책이 있는 하위 레딧을 선택했지만 일부 데이터에는 차별적이거나 해로운 언어가 포함될 수 있다. 데이터는 데이터 세트 작성자의 보기를 반영하지 않습니다. 이러한 하위 레딧의 레딧 사용자도 광범위한 모집단을 대표하지 않는다. 하위 레딧별 인구 통계 정보는 사용할 수 없지만 전체 레딧 사용자는 불균형적으로 남성이며 선진국, 서양 및 영어 사용 국가에서 왔습니다 ([Pew Research](https://www.pewresearch.org/internet/2013/07/03/6-of-online-adults-are-reddit-users/)). 이 데이터에 대해 학습된 모델을 사용하기 전에 이 점을 염두에 두십시오. ### 제한 사항 SHP의 선호도 레이블은 지시/질문이 주어졌을 때 한 응답이 다른 응답과 얼마나 도움이* 되는지 반영 하기 위한 것입니다. SHP는 좋은 독성 검출기를 배우는 데 필요한 독성 함량을 포함하도록 설계되지 않았기 때문에 위해 최소화에서 사용하기 위한 것이 아니다. 환경 설정 레이블이 더 적은 해를 나타내는 데이터를 찾는 경우 [Anthropic의 HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf)의 유해성 분할을 권장합니다. 또 다른 한계는 SHP에서 선호되는 응답이 반드시 더 사실적인 응답은 아니라는 것이다. 일부 논평은 그들의 반응을 정당화하기 위해 인용을 제공하지만 대부분은 그렇지 않다. 여기에는 `askhistorians` 하위 레딧과 같은 예외가 있으며, 이는 크게 조정되며 답변이 인용을 제공할 것으로 예상된다. SHP의 집단 선호도 라벨은 가중치가 없는 합계를 취하기 전에 사용자에게 각 코멘트에 독립적으로 투표하도록 요청하면 반드시 얻을 수 있는 것은 아니다. Reddit에 대한 주석 점수는 공개적이며 사용자 환경 설정에 영향을 미치는 것으로 알려져 있기 때문입니다. 높은 점수는 [(Muchnik et al., 2013)](https://pubmed.ncbi.nlm.nih.gov/23929980/)보다 긍정적인 표를 얻을 가능성을 높입니다. 이 "허딩 효과"가 사용자의 선호도를 일시적으로 또는 영구적으로 이동시키는지 여부는 불분명하다. 따라서, SHP가 집단적 인간 선호도를 반영하지만, SHP에 대해 훈련된 모델은 개별 선호도가 다르게 집계되는 설정으로 일반화되지 않을 수 있다(예를 들어, 사용자는 현재 코멘트 점수를 전혀 보지 않고 독립적으로 투표하고, 사용자는 부여 후 투표 등). 그렉 스토다드가 지적해줘서 고마워요 ## License Last updated: 03/01/2023 이 데이터 세트는 Reddit과 직접 통신 또는 서면 동의 없이 [Reddit API 사용 약관](https://docs.google.com/a/reddit.com/forms/d/e/1FAIpQLSezNdDNK1-P8mspSbmtC2r86Ee9ZRbC66u929cG2GX0T9UMyw/viewform)에 따라 Reddit을 스크래핑하여 만들었습니다. 사용 약관에 따라 "사용자 콘텐츠"는 Reddit이 아닌 사용자 자신이 소유하고 있으며 Reddit은 "사용자 콘텐츠를 복사 및 표시 하기 위해 독점적이지 않고 양도할 수 없으며 공개되지 않으며 취소할 수 있는 라이선스"를 부여 합니다. Reddit을 스크래핑 하 여 만든 데이터 집합은 연구 커뮤니티에서 널리 사용 됩니다. 예를 들어 Facebook AI 리서치는 Reddit에서 스크래핑 된 데이터를 사용 하 여 라이선스 없이 사용 하도록 만든 2019년 [ELI5](https://huggingface.co/datasets/eli5#source-data) 데이터 집합을 만들었습니다. 인류성 AI는 다른 방법론을 사용 하 여 환경 설정에 대 한 [Reddit을 스크래핑](https://arxiv.org/pdf/2112.00861.pdf) 합니다. 그러나이 데이터는 공개 되지 않았습니다. 정기적인 일정에서 Reddit의 전체 덤프를 사용할 수 있도록 하는 [PushShift Reddit 데이터 세트](https://arxiv.org/abs/2001.08435)도 라이선스 없이 사용할 수 있습니다 (알고 있는 범위). 우리는 책임을 지지 않으며 이 데이터 세트의 다운스트림 사용을 명시적으로 또는 암시적으로 지지하지 않는다. 우리는 향후 어느 시점에서든 SHP 데이터 세트와 이 라이선스를 수정할 수 있는 권한을 보유합니다. ## Contact 데이터에 대한 질문이 있는 경우 kawin@stanford.edu에 문의하십시오. 이 데이터 세트는 카윈 에타야라흐, 하이디(첸유) 장, 이중 왕 및 단 주라프스키에 의해 생성되었다. ## 인용 SHP는 다음 논문에서 제안한 기법을 이용하여 생성하였다. SHP 또는 스팀SHP 모델을 사용하는 경우 이 작업을 인용하십시오. ``` @InProceedings{pmlr-v162-ethayarajh22a, title = {Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information}, author = {Ethayarajh, Kawin and Choi, Yejin and Swayamdipta, Swabha}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {5988--6008}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, } ``` ## 참조 Ethayarajh, K., Choi, Y. & Swayamdipta, S. (2022). Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information. <i>Proceedings of the 39th International Conference on Machine Learning</i>, in <i>Proceedings of Machine Learning Research</i>. 162:5988-6008 Available from https://proceedings.mlr.press/v162/ethayarajh22a.html.
liuyanchen1015/MULTI_VALUE_wnli_linking_relcl	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 3249 num_examples: 17 - name: test num_bytes: 2742 num_examples: 9 - name: train num_bytes: 17600 num_examples: 92 download_size: 18391 dataset_size: 23591 --- # Dataset Card for "MULTI_VALUE_wnli_linking_relcl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deokhk/zh_wiki_sentences_1000000	--- dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 127836004 num_examples: 1000000 - name: dev num_bytes: 135625 num_examples: 1000 download_size: 88011343 dataset_size: 127971629 --- # Dataset Card for "zh_wiki_sentences_1000000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Birchlabs/openai-prm800k-phase2_train-stepwise-best	--- license: mit ---
open-llm-leaderboard/details_namirocks__student-model-13b-ep3	--- pretty_name: Evaluation run of namirocks/student-model-13b-ep3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [namirocks/student-model-13b-ep3](https://huggingface.co/namirocks/student-model-13b-ep3)\ \ 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_namirocks__student-model-13b-ep3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-30T01:37:20.077989](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__student-model-13b-ep3/blob/main/results_2023-12-30T01-37-20.077989.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\": 0.5621545466705268,\n\ \ \"acc_stderr\": 0.03351539520737431,\n \"acc_norm\": 0.5727655950528345,\n\ \ \"acc_norm_stderr\": 0.03442395762278095,\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871105,\n \"mc2\": 0.35003126952306707,\n\ \ \"mc2_stderr\": 0.014347219852780793\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.43856655290102387,\n \"acc_stderr\": 0.014500682618212865,\n\ \ \"acc_norm\": 0.46501706484641636,\n \"acc_norm_stderr\": 0.01457558392201966\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6061541525592511,\n\ \ \"acc_stderr\": 0.004876028037941937,\n \"acc_norm\": 0.8036247759410476,\n\ \ \"acc_norm_stderr\": 0.003964437012249992\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5855263157894737,\n \"acc_stderr\": 0.04008973785779206,\n\ \ \"acc_norm\": 0.5855263157894737,\n \"acc_norm_stderr\": 0.04008973785779206\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\": 0.6150943396226415,\n \"acc_stderr\": 0.02994649856769995,\n\ \ \"acc_norm\": 0.6150943396226415,\n \"acc_norm_stderr\": 0.02994649856769995\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6041666666666666,\n\ \ \"acc_stderr\": 0.04089465449325582,\n \"acc_norm\": 0.6041666666666666,\n\ \ \"acc_norm_stderr\": 0.04089465449325582\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.47,\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\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\"\ : 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\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\": 0.5664739884393064,\n\ \ \"acc_stderr\": 0.03778621079092056,\n \"acc_norm\": 0.5664739884393064,\n\ \ \"acc_norm_stderr\": 0.03778621079092056\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.04655010411319616,\n\ \ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.04655010411319616\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.43829787234042555,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.43829787234042555,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2982456140350877,\n\ \ \"acc_stderr\": 0.043036840335373146,\n \"acc_norm\": 0.2982456140350877,\n\ \ \"acc_norm_stderr\": 0.043036840335373146\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\ \ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3412698412698413,\n \"acc_stderr\": 0.024419234966819067,\n \"\ acc_norm\": 0.3412698412698413,\n \"acc_norm_stderr\": 0.024419234966819067\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.36507936507936506,\n\ \ \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n\ \ \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\"\ : 0.6483870967741936,\n \"acc_stderr\": 0.02716253782694846,\n \"\ acc_norm\": 0.6483870967741936,\n \"acc_norm_stderr\": 0.02716253782694846\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4975369458128079,\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\": 0.57,\n \"acc_stderr\": 0.04975698519562427,\n \"acc_norm\"\ : 0.57,\n \"acc_norm_stderr\": 0.04975698519562427\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.035679697722680495,\n\ \ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.035679697722680495\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6919191919191919,\n \"acc_stderr\": 0.03289477330098617,\n \"\ acc_norm\": 0.6919191919191919,\n \"acc_norm_stderr\": 0.03289477330098617\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8082901554404145,\n \"acc_stderr\": 0.028408953626245265,\n\ \ \"acc_norm\": 0.8082901554404145,\n \"acc_norm_stderr\": 0.028408953626245265\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5846153846153846,\n \"acc_stderr\": 0.02498535492310233,\n \ \ \"acc_norm\": 0.5846153846153846,\n \"acc_norm_stderr\": 0.02498535492310233\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948496,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948496\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6008403361344538,\n \"acc_stderr\": 0.03181110032413926,\n \ \ \"acc_norm\": 0.6008403361344538,\n \"acc_norm_stderr\": 0.03181110032413926\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\"\ : 0.7559633027522936,\n \"acc_stderr\": 0.0184152863514164,\n \"acc_norm\"\ : 0.7559633027522936,\n \"acc_norm_stderr\": 0.0184152863514164\n },\n\ \ \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.03388857118502326,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.03388857118502326\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\"\ : {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501947,\n\ \ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501947\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7552742616033755,\n \"acc_stderr\": 0.02798569938703642,\n \ \ \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.02798569938703642\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6870229007633588,\n \"acc_stderr\": 0.04066962905677698,\n\ \ \"acc_norm\": 0.6870229007633588,\n \"acc_norm_stderr\": 0.04066962905677698\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.03680350371286461,\n\ \ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.03680350371286461\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4017857142857143,\n\ \ \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.4017857142857143,\n\ \ \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.024414947304543678,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.024414947304543678\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7535121328224776,\n\ \ \"acc_stderr\": 0.015411308769686936,\n \"acc_norm\": 0.7535121328224776,\n\ \ \"acc_norm_stderr\": 0.015411308769686936\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.026296227915613663,\n\ \ \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.026296227915613663\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.33519553072625696,\n\ \ \"acc_stderr\": 0.01578800719018588,\n \"acc_norm\": 0.33519553072625696,\n\ \ \"acc_norm_stderr\": 0.01578800719018588\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6339869281045751,\n \"acc_stderr\": 0.027582811415159617,\n\ \ \"acc_norm\": 0.6339869281045751,\n \"acc_norm_stderr\": 0.027582811415159617\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6334405144694534,\n\ \ \"acc_stderr\": 0.027368078243971635,\n \"acc_norm\": 0.6334405144694534,\n\ \ \"acc_norm_stderr\": 0.027368078243971635\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.026869490744815247,\n\ \ \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.026869490744815247\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4326241134751773,\n \"acc_stderr\": 0.02955545423677886,\n \ \ \"acc_norm\": 0.4326241134751773,\n \"acc_norm_stderr\": 0.02955545423677886\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.423728813559322,\n\ \ \"acc_stderr\": 0.01262078515588599,\n \"acc_norm\": 0.423728813559322,\n\ \ \"acc_norm_stderr\": 0.01262078515588599\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.03035230339535196,\n\ \ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.03035230339535196\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5816993464052288,\n \"acc_stderr\": 0.019955975145835542,\n \ \ \"acc_norm\": 0.5816993464052288,\n \"acc_norm_stderr\": 0.019955975145835542\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\": 0.6489795918367347,\n \"acc_stderr\": 0.03055531675557364,\n\ \ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.03055531675557364\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7711442786069652,\n\ \ \"acc_stderr\": 0.029705284056772436,\n \"acc_norm\": 0.7711442786069652,\n\ \ \"acc_norm_stderr\": 0.029705284056772436\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4578313253012048,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338734,\n\ \ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338734\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871105,\n \"mc2\": 0.35003126952306707,\n\ \ \"mc2_stderr\": 0.014347219852780793\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7221783741120757,\n \"acc_stderr\": 0.012588918183871596\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/namirocks/student-model-13b-ep3 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_30T01_37_20.077989 path: - '/details_harness\|arc:challenge\|25_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-30T01-37-20.077989.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|gsm8k\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hellaswag\|10_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T01-37-20.077989.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-30T01-37-20.077989.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '/details_harness\|winogrande\|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-30T01-37-20.077989.parquet' - config_name: results data_files: - split: 2023_12_30T01_37_20.077989 path: - results_2023-12-30T01-37-20.077989.parquet - split: latest path: - results_2023-12-30T01-37-20.077989.parquet --- # Dataset Card for Evaluation run of namirocks/student-model-13b-ep3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [namirocks/student-model-13b-ep3](https://huggingface.co/namirocks/student-model-13b-ep3) 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_namirocks__student-model-13b-ep3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-30T01:37:20.077989](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__student-model-13b-ep3/blob/main/results_2023-12-30T01-37-20.077989.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": 0.5621545466705268, "acc_stderr": 0.03351539520737431, "acc_norm": 0.5727655950528345, "acc_norm_stderr": 0.03442395762278095, "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871105, "mc2": 0.35003126952306707, "mc2_stderr": 0.014347219852780793 }, "harness\|arc:challenge\|25": { "acc": 0.43856655290102387, "acc_stderr": 0.014500682618212865, "acc_norm": 0.46501706484641636, "acc_norm_stderr": 0.01457558392201966 }, "harness\|hellaswag\|10": { "acc": 0.6061541525592511, "acc_stderr": 0.004876028037941937, "acc_norm": 0.8036247759410476, "acc_norm_stderr": 0.003964437012249992 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5855263157894737, "acc_stderr": 0.04008973785779206, "acc_norm": 0.5855263157894737, "acc_norm_stderr": 0.04008973785779206 }, "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": 0.6150943396226415, "acc_stderr": 0.02994649856769995, "acc_norm": 0.6150943396226415, "acc_norm_stderr": 0.02994649856769995 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6041666666666666, "acc_stderr": 0.04089465449325582, "acc_norm": 0.6041666666666666, "acc_norm_stderr": 0.04089465449325582 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "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": 0.5664739884393064, "acc_stderr": 0.03778621079092056, "acc_norm": 0.5664739884393064, "acc_norm_stderr": 0.03778621079092056 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3235294117647059, "acc_stderr": 0.04655010411319616, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.04655010411319616 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.43829787234042555, "acc_stderr": 0.03243618636108102, "acc_norm": 0.43829787234042555, "acc_norm_stderr": 0.03243618636108102 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2982456140350877, "acc_stderr": 0.043036840335373146, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.043036840335373146 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.503448275862069, "acc_stderr": 0.04166567577101579, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.04166567577101579 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3412698412698413, "acc_stderr": 0.024419234966819067, "acc_norm": 0.3412698412698413, "acc_norm_stderr": 0.024419234966819067 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.36507936507936506, "acc_stderr": 0.04306241259127153, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.04306241259127153 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6483870967741936, "acc_stderr": 0.02716253782694846, "acc_norm": 0.6483870967741936, "acc_norm_stderr": 0.02716253782694846 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562427, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562427 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.703030303030303, "acc_stderr": 0.035679697722680495, "acc_norm": 0.703030303030303, "acc_norm_stderr": 0.035679697722680495 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6919191919191919, "acc_stderr": 0.03289477330098617, "acc_norm": 0.6919191919191919, "acc_norm_stderr": 0.03289477330098617 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8082901554404145, "acc_stderr": 0.028408953626245265, "acc_norm": 0.8082901554404145, "acc_norm_stderr": 0.028408953626245265 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5846153846153846, "acc_stderr": 0.02498535492310233, "acc_norm": 0.5846153846153846, "acc_norm_stderr": 0.02498535492310233 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948496, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948496 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6008403361344538, "acc_stderr": 0.03181110032413926, "acc_norm": 0.6008403361344538, "acc_norm_stderr": 0.03181110032413926 }, "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": 0.7559633027522936, "acc_stderr": 0.0184152863514164, "acc_norm": 0.7559633027522936, "acc_norm_stderr": 0.0184152863514164 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03388857118502326, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03388857118502326 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501947, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501947 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7552742616033755, "acc_stderr": 0.02798569938703642, "acc_norm": 0.7552742616033755, "acc_norm_stderr": 0.02798569938703642 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.672645739910314, "acc_stderr": 0.031493846709941306, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.031493846709941306 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6870229007633588, "acc_stderr": 0.04066962905677698, "acc_norm": 0.6870229007633588, "acc_norm_stderr": 0.04066962905677698 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6748466257668712, "acc_stderr": 0.03680350371286461, "acc_norm": 0.6748466257668712, "acc_norm_stderr": 0.03680350371286461 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4017857142857143, "acc_stderr": 0.04653333146973646, "acc_norm": 0.4017857142857143, "acc_norm_stderr": 0.04653333146973646 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.024414947304543678, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.024414947304543678 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7535121328224776, "acc_stderr": 0.015411308769686936, "acc_norm": 0.7535121328224776, "acc_norm_stderr": 0.015411308769686936 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6069364161849711, "acc_stderr": 0.026296227915613663, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.026296227915613663 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.33519553072625696, "acc_stderr": 0.01578800719018588, "acc_norm": 0.33519553072625696, "acc_norm_stderr": 0.01578800719018588 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6339869281045751, "acc_stderr": 0.027582811415159617, "acc_norm": 0.6339869281045751, "acc_norm_stderr": 0.027582811415159617 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6334405144694534, "acc_stderr": 0.027368078243971635, "acc_norm": 0.6334405144694534, "acc_norm_stderr": 0.027368078243971635 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.026869490744815247, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.026869490744815247 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4326241134751773, "acc_stderr": 0.02955545423677886, "acc_norm": 0.4326241134751773, "acc_norm_stderr": 0.02955545423677886 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.423728813559322, "acc_stderr": 0.01262078515588599, "acc_norm": 0.423728813559322, "acc_norm_stderr": 0.01262078515588599 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5183823529411765, "acc_stderr": 0.03035230339535196, "acc_norm": 0.5183823529411765, "acc_norm_stderr": 0.03035230339535196 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5816993464052288, "acc_stderr": 0.019955975145835542, "acc_norm": 0.5816993464052288, "acc_norm_stderr": 0.019955975145835542 }, "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": 0.6489795918367347, "acc_stderr": 0.03055531675557364, "acc_norm": 0.6489795918367347, "acc_norm_stderr": 0.03055531675557364 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7711442786069652, "acc_stderr": 0.029705284056772436, "acc_norm": 0.7711442786069652, "acc_norm_stderr": 0.029705284056772436 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4578313253012048, "acc_stderr": 0.038786267710023595, "acc_norm": 0.4578313253012048, "acc_norm_stderr": 0.038786267710023595 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.783625730994152, "acc_stderr": 0.03158149539338734, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.03158149539338734 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871105, "mc2": 0.35003126952306707, "mc2_stderr": 0.014347219852780793 }, "harness\|winogrande\|5": { "acc": 0.7221783741120757, "acc_stderr": 0.012588918183871596 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
tyzhu/squad_wrong_title_v3_train_10_eval_10	--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 276687 num_examples: 184 - name: validation num_bytes: 64754 num_examples: 68 download_size: 71442 dataset_size: 341441 --- # Dataset Card for "squad_wrong_title_v3_train_10_eval_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
divers/requirement-question	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: index dtype: int64 - name: job_requirement dtype: string - name: questions dtype: string splits: - name: train num_bytes: 35480682 num_examples: 23237 download_size: 4168927 dataset_size: 35480682 --- # Dataset Card for "requirement-question" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Kaue123456/JoaoGriloMatheusNachtergaele	--- license: openrail ---
arthurmluz/cstnews_data-temario_results	--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 69932 num_examples: 16 download_size: 0 dataset_size: 69932 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "cstnews_data-temario_results" rouge= {'rouge1': 0.5207584715132082, 'rouge2': 0.34711381882009107, 'rougeL': 0.38095639884621346, 'rougeLsum': 0.38095639884621346} bert= {'precision': 0.7428307943046093, 'recall': 0.8364794515073299, 'f1': 0.7866528294980526} mover = 0.6287250343090405
open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3	--- pretty_name: Evaluation run of OpenAssistant/stablelm-7b-sft-v7-epoch-3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [OpenAssistant/stablelm-7b-sft-v7-epoch-3](https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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 agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-13T03:23:25.661445](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3/blob/main/results_2023-10-13T03-23-25.661445.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.05578859060402685,\n\ \ \"em_stderr\": 0.0023504280872280073,\n \"f1\": 0.10613569630872476,\n\ \ \"f1_stderr\": 0.0026144580255279513,\n \"acc\": 0.27616530425036784,\n\ \ \"acc_stderr\": 0.007839405520583978\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.05578859060402685,\n \"em_stderr\": 0.0023504280872280073,\n\ \ \"f1\": 0.10613569630872476,\n \"f1_stderr\": 0.0026144580255279513\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0037907505686125853,\n \ \ \"acc_stderr\": 0.0016927007401501943\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5485398579321231,\n \"acc_stderr\": 0.01398611030101776\n\ \ }\n}\n```" repo_url: https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3 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_07_19T17_06_42.731727 path: - '/details_harness\|arc:challenge\|25_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|arc:challenge\|25_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T17:07:54.588127.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_13T03_23_25.661445 path: - '/details_harness\|drop\|3_2023-10-13T03-23-25.661445.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-13T03-23-25.661445.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_13T03_23_25.661445 path: - '/details_harness\|gsm8k\|5_2023-10-13T03-23-25.661445.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-13T03-23-25.661445.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hellaswag\|10_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hellaswag\|10_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T17:06:42.731727.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T17:07:54.588127.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T17_06_42.731727 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T17:07:54.588127.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_13T03_23_25.661445 path: - '/details_harness\|winogrande\|5_2023-10-13T03-23-25.661445.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-13T03-23-25.661445.parquet' - config_name: results data_files: - split: 2023_07_19T17_06_42.731727 path: - results_2023-07-19T17:06:42.731727.parquet - split: 2023_07_19T17_07_54.588127 path: - results_2023-07-19T17:07:54.588127.parquet - split: 2023_10_13T03_23_25.661445 path: - results_2023-10-13T03-23-25.661445.parquet - split: latest path: - results_2023-10-13T03-23-25.661445.parquet --- # Dataset Card for Evaluation run of OpenAssistant/stablelm-7b-sft-v7-epoch-3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [OpenAssistant/stablelm-7b-sft-v7-epoch-3](https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-13T03:23:25.661445](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3/blob/main/results_2023-10-13T03-23-25.661445.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.05578859060402685, "em_stderr": 0.0023504280872280073, "f1": 0.10613569630872476, "f1_stderr": 0.0026144580255279513, "acc": 0.27616530425036784, "acc_stderr": 0.007839405520583978 }, "harness\|drop\|3": { "em": 0.05578859060402685, "em_stderr": 0.0023504280872280073, "f1": 0.10613569630872476, "f1_stderr": 0.0026144580255279513 }, "harness\|gsm8k\|5": { "acc": 0.0037907505686125853, "acc_stderr": 0.0016927007401501943 }, "harness\|winogrande\|5": { "acc": 0.5485398579321231, "acc_stderr": 0.01398611030101776 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
atmallen/neg_companies_azaria_mitchell	--- dataset_info: features: - name: statement dtype: string - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 75670.4 num_examples: 880 - name: test num_bytes: 18917.6 num_examples: 220 download_size: 29413 dataset_size: 94588.0 --- # Dataset Card for "neg_companies_azaria_mitchell" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dongyoung4091/shp-generated_flan_t5_large_with_features	--- dataset_info: features: - name: response dtype: string - name: prompt dtype: string - name: helpfulness dtype: int64 - name: specificity dtype: int64 - name: intent dtype: int64 - name: factuality dtype: int64 - name: easy-to-understand dtype: int64 - name: relevance dtype: int64 - name: readability dtype: int64 - name: enough-detail dtype: int64 - name: 'biased:' dtype: int64 - name: fail-to-consider-individual-preferences dtype: int64 - name: repetetive dtype: int64 - name: fail-to-consider-context dtype: int64 - name: too-long dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1736538 num_examples: 1500 download_size: 215337 dataset_size: 1736538 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "shp-generated_flan_t5_large_with_features" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_yyh0901__lloma_step200	--- pretty_name: Evaluation run of yyh0901/lloma_step200 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [yyh0901/lloma_step200](https://huggingface.co/yyh0901/lloma_step200) 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_yyh0901__lloma_step200\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-06T13:05:40.214845](https://huggingface.co/datasets/open-llm-leaderboard/details_yyh0901__lloma_step200/blob/main/results_2024-04-06T13-05-40.214845.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\": 0.4370638206369057,\n\ \ \"acc_stderr\": 0.034251250239833685,\n \"acc_norm\": 0.44330275084620024,\n\ \ \"acc_norm_stderr\": 0.035090552757218396,\n \"mc1\": 0.23378212974296206,\n\ \ \"mc1_stderr\": 0.014816195991931578,\n \"mc2\": 0.398074913504716,\n\ \ \"mc2_stderr\": 0.01370017096726305\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.46245733788395904,\n \"acc_stderr\": 0.01457014449507558,\n\ \ \"acc_norm\": 0.5068259385665529,\n \"acc_norm_stderr\": 0.014610029151379813\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.571400119498108,\n\ \ \"acc_stderr\": 0.004938643787869547,\n \"acc_norm\": 0.7714598685520813,\n\ \ \"acc_norm_stderr\": 0.004190341541141985\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\": 0.42962962962962964,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.4342105263157895,\n \"acc_stderr\": 0.0403356566784832,\n\ \ \"acc_norm\": 0.4342105263157895,\n \"acc_norm_stderr\": 0.0403356566784832\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.44528301886792454,\n \"acc_stderr\": 0.030588052974270658,\n\ \ \"acc_norm\": 0.44528301886792454,\n \"acc_norm_stderr\": 0.030588052974270658\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4166666666666667,\n\ \ \"acc_stderr\": 0.04122728707651282,\n \"acc_norm\": 0.4166666666666667,\n\ \ \"acc_norm_stderr\": 0.04122728707651282\n },\n \"harness\|hendrycksTest-college_chemistry\|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-college_computer_science\|5\": {\n \"\ acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.3,\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\": 0.36416184971098264,\n\ \ \"acc_stderr\": 0.03669072477416908,\n \"acc_norm\": 0.36416184971098264,\n\ \ \"acc_norm_stderr\": 0.03669072477416908\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179964,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179964\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n\ \ \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4425531914893617,\n \"acc_stderr\": 0.032469569197899575,\n\ \ \"acc_norm\": 0.4425531914893617,\n \"acc_norm_stderr\": 0.032469569197899575\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2894736842105263,\n\ \ \"acc_stderr\": 0.04266339443159393,\n \"acc_norm\": 0.2894736842105263,\n\ \ \"acc_norm_stderr\": 0.04266339443159393\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.040824829046386284,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.040824829046386284\n \ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708624,\n \"\ acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708624\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.25396825396825395,\n\ \ \"acc_stderr\": 0.038932596106046734,\n \"acc_norm\": 0.25396825396825395,\n\ \ \"acc_norm_stderr\": 0.038932596106046734\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\": 0.4645161290322581,\n\ \ \"acc_stderr\": 0.028372287797962956,\n \"acc_norm\": 0.4645161290322581,\n\ \ \"acc_norm_stderr\": 0.028372287797962956\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.31527093596059114,\n \"acc_stderr\": 0.03269080871970187,\n\ \ \"acc_norm\": 0.31527093596059114,\n \"acc_norm_stderr\": 0.03269080871970187\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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-high_school_european_history\|5\"\ : {\n \"acc\": 0.5212121212121212,\n \"acc_stderr\": 0.03900828913737302,\n\ \ \"acc_norm\": 0.5212121212121212,\n \"acc_norm_stderr\": 0.03900828913737302\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.4797979797979798,\n \"acc_stderr\": 0.03559443565563919,\n \"\ acc_norm\": 0.4797979797979798,\n \"acc_norm_stderr\": 0.03559443565563919\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.6424870466321243,\n \"acc_stderr\": 0.034588160421810114,\n\ \ \"acc_norm\": 0.6424870466321243,\n \"acc_norm_stderr\": 0.034588160421810114\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.441025641025641,\n \"acc_stderr\": 0.025174048384000745,\n \ \ \"acc_norm\": 0.441025641025641,\n \"acc_norm_stderr\": 0.025174048384000745\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340496,\n \ \ \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340496\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.031041941304059278,\n\ \ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.031041941304059278\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389023,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389023\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.6110091743119266,\n \"acc_stderr\": 0.020902300887392873,\n \"\ acc_norm\": 0.6110091743119266,\n \"acc_norm_stderr\": 0.020902300887392873\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.27314814814814814,\n \"acc_stderr\": 0.03038805130167812,\n \"\ acc_norm\": 0.27314814814814814,\n \"acc_norm_stderr\": 0.03038805130167812\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.5245098039215687,\n \"acc_stderr\": 0.03505093194348798,\n \"\ acc_norm\": 0.5245098039215687,\n \"acc_norm_stderr\": 0.03505093194348798\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.5780590717299579,\n \"acc_stderr\": 0.032148146302403695,\n \ \ \"acc_norm\": 0.5780590717299579,\n \"acc_norm_stderr\": 0.032148146302403695\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5201793721973094,\n\ \ \"acc_stderr\": 0.033530461674123005,\n \"acc_norm\": 0.5201793721973094,\n\ \ \"acc_norm_stderr\": 0.033530461674123005\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5419847328244275,\n \"acc_stderr\": 0.04369802690578756,\n\ \ \"acc_norm\": 0.5419847328244275,\n \"acc_norm_stderr\": 0.04369802690578756\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.5950413223140496,\n \"acc_stderr\": 0.04481137755942469,\n \"\ acc_norm\": 0.5950413223140496,\n \"acc_norm_stderr\": 0.04481137755942469\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.49074074074074076,\n\ \ \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.49074074074074076,\n\ \ \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.4294478527607362,\n \"acc_stderr\": 0.038890666191127216,\n\ \ \"acc_norm\": 0.4294478527607362,\n \"acc_norm_stderr\": 0.038890666191127216\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.38392857142857145,\n\ \ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\ \ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.3592233009708738,\n \"acc_stderr\": 0.047504583990416946,\n\ \ \"acc_norm\": 0.3592233009708738,\n \"acc_norm_stderr\": 0.047504583990416946\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.6623931623931624,\n\ \ \"acc_stderr\": 0.030980296992618558,\n \"acc_norm\": 0.6623931623931624,\n\ \ \"acc_norm_stderr\": 0.030980296992618558\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.5887611749680716,\n\ \ \"acc_stderr\": 0.01759597190805657,\n \"acc_norm\": 0.5887611749680716,\n\ \ \"acc_norm_stderr\": 0.01759597190805657\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.4913294797687861,\n \"acc_stderr\": 0.026915047355369804,\n\ \ \"acc_norm\": 0.4913294797687861,\n \"acc_norm_stderr\": 0.026915047355369804\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.42810457516339867,\n \"acc_stderr\": 0.028332397483664274,\n\ \ \"acc_norm\": 0.42810457516339867,\n \"acc_norm_stderr\": 0.028332397483664274\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5401929260450161,\n\ \ \"acc_stderr\": 0.028306190403305696,\n \"acc_norm\": 0.5401929260450161,\n\ \ \"acc_norm_stderr\": 0.028306190403305696\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.027815973433878014,\n\ \ \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.027815973433878014\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.35106382978723405,\n \"acc_stderr\": 0.028473501272963764,\n \ \ \"acc_norm\": 0.35106382978723405,\n \"acc_norm_stderr\": 0.028473501272963764\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3539765319426336,\n\ \ \"acc_stderr\": 0.01221350473173164,\n \"acc_norm\": 0.3539765319426336,\n\ \ \"acc_norm_stderr\": 0.01221350473173164\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.030352303395351964,\n\ \ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.030352303395351964\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.4215686274509804,\n \"acc_stderr\": 0.019977422600227467,\n \ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.019977422600227467\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5181818181818182,\n\ \ \"acc_stderr\": 0.04785964010794915,\n \"acc_norm\": 0.5181818181818182,\n\ \ \"acc_norm_stderr\": 0.04785964010794915\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.4204081632653061,\n \"acc_stderr\": 0.03160106993449604,\n\ \ \"acc_norm\": 0.4204081632653061,\n \"acc_norm_stderr\": 0.03160106993449604\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.5920398009950248,\n\ \ \"acc_stderr\": 0.03475116365194092,\n \"acc_norm\": 0.5920398009950248,\n\ \ \"acc_norm_stderr\": 0.03475116365194092\n },\n \"harness\|hendrycksTest-us_foreign_policy\|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-virology\|5\": {\n \"acc\": 0.35542168674698793,\n\ \ \"acc_stderr\": 0.03726214354322415,\n \"acc_norm\": 0.35542168674698793,\n\ \ \"acc_norm_stderr\": 0.03726214354322415\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.6549707602339181,\n \"acc_stderr\": 0.036459813773888065,\n\ \ \"acc_norm\": 0.6549707602339181,\n \"acc_norm_stderr\": 0.036459813773888065\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.23378212974296206,\n\ \ \"mc1_stderr\": 0.014816195991931578,\n \"mc2\": 0.398074913504716,\n\ \ \"mc2_stderr\": 0.01370017096726305\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7087608524072613,\n \"acc_stderr\": 0.012769029305370697\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.04169825625473844,\n \ \ \"acc_stderr\": 0.005506205058175746\n }\n}\n```" repo_url: https://huggingface.co/yyh0901/lloma_step200 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|arc:challenge\|25_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-06T13-05-40.214845.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|gsm8k\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hellaswag\|10_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T13-05-40.214845.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-06T13-05-40.214845.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '/details_harness\|winogrande\|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-06T13-05-40.214845.parquet' - config_name: results data_files: - split: 2024_04_06T13_05_40.214845 path: - results_2024-04-06T13-05-40.214845.parquet - split: latest path: - results_2024-04-06T13-05-40.214845.parquet --- # Dataset Card for Evaluation run of yyh0901/lloma_step200 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [yyh0901/lloma_step200](https://huggingface.co/yyh0901/lloma_step200) 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_yyh0901__lloma_step200", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-06T13:05:40.214845](https://huggingface.co/datasets/open-llm-leaderboard/details_yyh0901__lloma_step200/blob/main/results_2024-04-06T13-05-40.214845.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": 0.4370638206369057, "acc_stderr": 0.034251250239833685, "acc_norm": 0.44330275084620024, "acc_norm_stderr": 0.035090552757218396, "mc1": 0.23378212974296206, "mc1_stderr": 0.014816195991931578, "mc2": 0.398074913504716, "mc2_stderr": 0.01370017096726305 }, "harness\|arc:challenge\|25": { "acc": 0.46245733788395904, "acc_stderr": 0.01457014449507558, "acc_norm": 0.5068259385665529, "acc_norm_stderr": 0.014610029151379813 }, "harness\|hellaswag\|10": { "acc": 0.571400119498108, "acc_stderr": 0.004938643787869547, "acc_norm": 0.7714598685520813, "acc_norm_stderr": 0.004190341541141985 }, "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": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.4342105263157895, "acc_stderr": 0.0403356566784832, "acc_norm": 0.4342105263157895, "acc_norm_stderr": 0.0403356566784832 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.44528301886792454, "acc_stderr": 0.030588052974270658, "acc_norm": 0.44528301886792454, "acc_norm_stderr": 0.030588052974270658 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4166666666666667, "acc_stderr": 0.04122728707651282, "acc_norm": 0.4166666666666667, "acc_norm_stderr": 0.04122728707651282 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.36416184971098264, "acc_stderr": 0.03669072477416908, "acc_norm": 0.36416184971098264, "acc_norm_stderr": 0.03669072477416908 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179964, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179964 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4425531914893617, "acc_stderr": 0.032469569197899575, "acc_norm": 0.4425531914893617, "acc_norm_stderr": 0.032469569197899575 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159393, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159393 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4, "acc_stderr": 0.040824829046386284, "acc_norm": 0.4, "acc_norm_stderr": 0.040824829046386284 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.26455026455026454, "acc_stderr": 0.022717467897708624, "acc_norm": 0.26455026455026454, "acc_norm_stderr": 0.022717467897708624 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.25396825396825395, "acc_stderr": 0.038932596106046734, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.038932596106046734 }, "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": 0.4645161290322581, "acc_stderr": 0.028372287797962956, "acc_norm": 0.4645161290322581, "acc_norm_stderr": 0.028372287797962956 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.31527093596059114, "acc_stderr": 0.03269080871970187, "acc_norm": 0.31527093596059114, "acc_norm_stderr": 0.03269080871970187 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.5212121212121212, "acc_stderr": 0.03900828913737302, "acc_norm": 0.5212121212121212, "acc_norm_stderr": 0.03900828913737302 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.4797979797979798, "acc_stderr": 0.03559443565563919, "acc_norm": 0.4797979797979798, "acc_norm_stderr": 0.03559443565563919 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6424870466321243, "acc_stderr": 0.034588160421810114, "acc_norm": 0.6424870466321243, "acc_norm_stderr": 0.034588160421810114 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.441025641025641, "acc_stderr": 0.025174048384000745, "acc_norm": 0.441025641025641, "acc_norm_stderr": 0.025174048384000745 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2851851851851852, "acc_stderr": 0.027528599210340496, "acc_norm": 0.2851851851851852, "acc_norm_stderr": 0.027528599210340496 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.35294117647058826, "acc_stderr": 0.031041941304059278, "acc_norm": 0.35294117647058826, "acc_norm_stderr": 0.031041941304059278 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389023, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389023 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6110091743119266, "acc_stderr": 0.020902300887392873, "acc_norm": 0.6110091743119266, "acc_norm_stderr": 0.020902300887392873 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.27314814814814814, "acc_stderr": 0.03038805130167812, "acc_norm": 0.27314814814814814, "acc_norm_stderr": 0.03038805130167812 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5245098039215687, "acc_stderr": 0.03505093194348798, "acc_norm": 0.5245098039215687, "acc_norm_stderr": 0.03505093194348798 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5780590717299579, "acc_stderr": 0.032148146302403695, "acc_norm": 0.5780590717299579, "acc_norm_stderr": 0.032148146302403695 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5201793721973094, "acc_stderr": 0.033530461674123005, "acc_norm": 0.5201793721973094, "acc_norm_stderr": 0.033530461674123005 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5419847328244275, "acc_stderr": 0.04369802690578756, "acc_norm": 0.5419847328244275, "acc_norm_stderr": 0.04369802690578756 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.5950413223140496, "acc_stderr": 0.04481137755942469, "acc_norm": 0.5950413223140496, "acc_norm_stderr": 0.04481137755942469 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.49074074074074076, "acc_stderr": 0.04832853553437055, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.04832853553437055 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.4294478527607362, "acc_stderr": 0.038890666191127216, "acc_norm": 0.4294478527607362, "acc_norm_stderr": 0.038890666191127216 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness\|hendrycksTest-management\|5": { "acc": 0.3592233009708738, "acc_stderr": 0.047504583990416946, "acc_norm": 0.3592233009708738, "acc_norm_stderr": 0.047504583990416946 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6623931623931624, "acc_stderr": 0.030980296992618558, "acc_norm": 0.6623931623931624, "acc_norm_stderr": 0.030980296992618558 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5887611749680716, "acc_stderr": 0.01759597190805657, "acc_norm": 0.5887611749680716, "acc_norm_stderr": 0.01759597190805657 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.4913294797687861, "acc_stderr": 0.026915047355369804, "acc_norm": 0.4913294797687861, "acc_norm_stderr": 0.026915047355369804 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.42810457516339867, "acc_stderr": 0.028332397483664274, "acc_norm": 0.42810457516339867, "acc_norm_stderr": 0.028332397483664274 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5401929260450161, "acc_stderr": 0.028306190403305696, "acc_norm": 0.5401929260450161, "acc_norm_stderr": 0.028306190403305696 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.49074074074074076, "acc_stderr": 0.027815973433878014, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.027815973433878014 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.35106382978723405, "acc_stderr": 0.028473501272963764, "acc_norm": 0.35106382978723405, "acc_norm_stderr": 0.028473501272963764 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3539765319426336, "acc_stderr": 0.01221350473173164, "acc_norm": 0.3539765319426336, "acc_norm_stderr": 0.01221350473173164 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5183823529411765, "acc_stderr": 0.030352303395351964, "acc_norm": 0.5183823529411765, "acc_norm_stderr": 0.030352303395351964 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.019977422600227467, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.019977422600227467 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5181818181818182, "acc_stderr": 0.04785964010794915, "acc_norm": 0.5181818181818182, "acc_norm_stderr": 0.04785964010794915 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.4204081632653061, "acc_stderr": 0.03160106993449604, "acc_norm": 0.4204081632653061, "acc_norm_stderr": 0.03160106993449604 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5920398009950248, "acc_stderr": 0.03475116365194092, "acc_norm": 0.5920398009950248, "acc_norm_stderr": 0.03475116365194092 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.35542168674698793, "acc_stderr": 0.03726214354322415, "acc_norm": 0.35542168674698793, "acc_norm_stderr": 0.03726214354322415 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6549707602339181, "acc_stderr": 0.036459813773888065, "acc_norm": 0.6549707602339181, "acc_norm_stderr": 0.036459813773888065 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23378212974296206, "mc1_stderr": 0.014816195991931578, "mc2": 0.398074913504716, "mc2_stderr": 0.01370017096726305 }, "harness\|winogrande\|5": { "acc": 0.7087608524072613, "acc_stderr": 0.012769029305370697 }, "harness\|gsm8k\|5": { "acc": 0.04169825625473844, "acc_stderr": 0.005506205058175746 } } ``` ## 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]
tvergho/audio-diffusion-512	--- dataset_info: features: - name: image dtype: image - name: audio_file dtype: string - name: slice dtype: int16 splits: - name: train num_bytes: 896860831.5 num_examples: 6964 download_size: 895892605 dataset_size: 896860831.5 --- # Dataset Card for "audio-diffusion-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KnutJaegersberg/Deita-6k	--- license: mit --- English subset of the data.
rai-sandeep/dataset_full_v3	--- dataset_info: features: - name: doctype dtype: string - name: section dtype: string - name: topic dtype: string - name: content dtype: string splits: - name: train num_bytes: 27897 num_examples: 26 download_size: 19998 dataset_size: 27897 --- # Dataset Card for "dataset_full_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jamespratama/tutorial-platypus-llamma	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4251526 num_examples: 1000 download_size: 2253085 dataset_size: 4251526 configs: - config_name: default data_files: - split: train path: data/train-* ---
lilacai/lilac-HellaSwag	--- tags: - Lilac --- # lilac/HellaSwag This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/Rowan/hellaswag](https://huggingface.co/datasets/Rowan/hellaswag) To download the dataset to a local directory: ```bash lilac download lilacai/lilac-HellaSwag ``` or from python with: ```py ll.download("lilacai/lilac-HellaSwag") ```
Elfsong/patient_info	--- configs: - config_name: default data_files: - split: anxiety path: data/anxiety-* - split: depression path: data/depression-* - split: ptsd path: data/ptsd-* - split: bipolar path: data/bipolar-* - split: substance_misuse path: data/substance_misuse-* - split: eating_disorders path: data/eating_disorders-* - split: alcohol_consumption path: data/alcohol_consumption-* dataset_info: features: - name: url dtype: string - name: comments list: - name: author_from sequence: string - name: author_to sequence: string - name: comments list: - name: author_from sequence: string - name: author_to sequence: string - name: content sequence: string - name: date sequence: string - name: content sequence: string - name: date sequence: string - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: content dtype: string - name: author dtype: string splits: - name: anxiety num_bytes: 143006120 num_examples: 27393 - name: depression num_bytes: 49953142 num_examples: 6982 - name: ptsd num_bytes: 1626957 num_examples: 349 - name: bipolar num_bytes: 3087512 num_examples: 474 - name: substance_misuse num_bytes: 1406369 num_examples: 195 - name: eating_disorders num_bytes: 1294592 num_examples: 233 - name: alcohol_consumption num_bytes: 21540333 num_examples: 1855 download_size: 109169290 dataset_size: 221915025 --- # Dataset Card for "patient_info" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sid-th26/prelims_all_questions	--- dataset_info: features: - name: section_name dtype: string - name: sub_section_name dtype: string - name: topic_name dtype: string - name: Question dtype: string - name: Option_A dtype: string - name: Option_B dtype: string - name: Option_C dtype: string - name: Option_D dtype: string - name: explanation dtype: string - name: difficulty dtype: string - name: answer dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 229012652 num_examples: 92171 download_size: 109175296 dataset_size: 229012652 configs: - config_name: default data_files: - split: train path: data/train-* ---
nuprl/MultiPL-E-synthetic-solutions	--- dataset_info: features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: solution dtype: string splits: - name: train num_bytes: 2185285 num_examples: 2624 download_size: 891673 dataset_size: 2185285 license: openrail language: - en pretty_name: MultiPL-E Synthetic Solutions --- # Dataset Card This is a dataset of partial solutions to the HumanEval and MBPP code generation benchmarks tranlated into 18+ programming languages. The original benchmark problems were in Python, and we build the dataset as follows: 1. We translate the prompts into a new language using MultiPL-E; 2. We use code-davinci-002 to generate 200 completions for each problem at temperature 0.8; 3. We select a working solution (if one exists) for each problem-language pair. [This notebook](https://github.com/nuprl/MultiPL-E/blob/main/notebooks/build_synthetic_solutions_dataset.ipynb) carried out the steps described above. Note that the dataset does not have solutions for every problem-language pair, since code-davinci-002 cannot produce a correct solution to every problem.
Joedwoo/NTT_version_1.0	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 3595 num_examples: 13 download_size: 4937 dataset_size: 3595 configs: - config_name: default data_files: - split: train path: data/train-* ---
cmarvolo/auto_fl	--- license: apache-2.0 task_categories: - text-generation language: - en tags: - not-for-all-audiences size_categories: - n<1K ---
insanemyrr/test-diploma-lucchi-cropped-new-mix-biggest	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': testing '1': training splits: - name: train num_bytes: 299779032.96 num_examples: 3960 - name: test num_bytes: 299751233.76 num_examples: 3960 download_size: 599433953 dataset_size: 599530266.72 --- # Dataset Card for "test-diploma-lucchi-cropped-new-mix-biggest" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-28000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 664600 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
polinaeterna/push_to_hub_many_configs	--- builder_configs: - config_name: custom data_files: - split: train pattern: custom/train-* - split: random pattern: custom/random-* - config_name: default data_files: - split: train pattern: data/train-* - split: random pattern: data/random-* dataset_info: - config_name: custom features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 1600 num_examples: 100 - name: random num_bytes: 160 num_examples: 10 download_size: 3650 dataset_size: 1760 - config_name: default features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 1600 num_examples: 100 - name: random num_bytes: 800 num_examples: 50 download_size: 4042 dataset_size: 2400 --- # Dataset Card for "push_to_hub_many_configs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/oosaki_amana_theidolmstershinycolors	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of oosaki_amana/大崎甘奈 (THE iDOLM@STER: SHINY COLORS) This is the dataset of oosaki_amana/大崎甘奈 (THE iDOLM@STER: SHINY COLORS), containing 500 images and their tags. The core tags of this character are `long_hair, bangs, brown_hair, yellow_eyes, breasts, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:--------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 978.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 475.79 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1311 \| 1.04 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 815.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1311 \| 1.66 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/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/oosaki_amana_theidolmstershinycolors', 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 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blush, cleavage, collarbone, looking_at_viewer, solo, bare_shoulders, necklace, long_sleeves, off-shoulder_sweater, sitting, closed_mouth, double_bun, dress, earrings, smile, swept_bangs \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, solo, blush, smile, collarbone, white_background, closed_mouth, simple_background, upper_body, bare_shoulders, brown_eyes, cleavage, choker, heart, sleeveless, white_dress \| \| 2 \| 12 \| ![](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, looking_at_viewer, necktie, plaid_skirt, school_uniform, solo, pleated_skirt, blush, open_mouth, :d, long_sleeves, white_shirt, braid, simple_background, sweater, blazer, collared_shirt, outdoors, petals, red_hair, white_background \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, collarbone, eyewear_on_head, heart-shaped_eyewear, navel, red_bikini, sunglasses, black_choker, floral_print, looking_at_viewer, simple_background, solo, swept_bangs, blush, bracelet, cleavage, earrings, necklace, one_eye_closed, open_mouth, white_background, :d, bare_shoulders, groin, thighs \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blush, gloves, looking_at_viewer, solo, white_coat, braid, open_mouth, smile, fur_hat, long_sleeves, snowing, white_headwear, winter_clothes, fur_trim, hat_bow, upper_body \| \| 5 \| 5 \| ![](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, blue_sky, blush, day, looking_at_viewer, navel, outdoors, solo, armpits, arms_up, cloud, midriff, arms_behind_head, cowboy_shot, hairband, :d, bikini_under_clothes, blue_shorts, brown_eyes, collarbone, denim_shorts, frills, hair_bow, open_mouth, ribbon, short_shorts, swept_bangs, tied_shirt \| \| 6 \| 10 \| ![](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, blush, looking_at_viewer, hair_ornament, obi, print_kimono, solo, floral_print, side_ponytail, sidelocks, hair_between_eyes, open_mouth, pink_kimono, wide_sleeves, :d, long_sleeves, yukata, blurry, swept_bangs, upper_body \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, blush, collarbone, looking_at_viewer, race_queen, solo, thigh_boots, thighhighs, black_choker, cleavage, hair_ribbon, holding_umbrella, navel, red_hair, belt, black_skirt, high_ponytail, miniskirt, open_mouth, smile, standing, swept_bangs, wrist_cuffs, bare_shoulders, black_footwear, full_body, ground_vehicle, hoop_earrings, midriff, mismatched_footwear, mismatched_legwear, motor_vehicle, nail_polish, official_alternate_costume, outdoors, sidelocks, sleeveless, tube_top \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| cleavage \| collarbone \| looking_at_viewer \| solo \| bare_shoulders \| necklace \| long_sleeves \| off-shoulder_sweater \| sitting \| closed_mouth \| double_bun \| dress \| earrings \| smile \| swept_bangs \| white_background \| simple_background \| upper_body \| brown_eyes \| choker \| heart \| sleeveless \| white_dress \| necktie \| plaid_skirt \| school_uniform \| pleated_skirt \| open_mouth \| :d \| white_shirt \| braid \| sweater \| blazer \| collared_shirt \| outdoors \| petals \| red_hair \| eyewear_on_head \| heart-shaped_eyewear \| navel \| red_bikini \| sunglasses \| black_choker \| floral_print \| bracelet \| one_eye_closed \| groin \| thighs \| gloves \| white_coat \| fur_hat \| snowing \| white_headwear \| winter_clothes \| fur_trim \| hat_bow \| blue_sky \| day \| armpits \| arms_up \| cloud \| midriff \| arms_behind_head \| cowboy_shot \| hairband \| bikini_under_clothes \| blue_shorts \| denim_shorts \| frills \| hair_bow \| ribbon \| short_shorts \| tied_shirt \| hair_ornament \| obi \| print_kimono \| side_ponytail \| sidelocks \| hair_between_eyes \| pink_kimono \| wide_sleeves \| yukata \| blurry \| race_queen \| thigh_boots \| thighhighs \| hair_ribbon \| holding_umbrella \| belt \| black_skirt \| high_ponytail \| miniskirt \| standing \| wrist_cuffs \| black_footwear \| full_body \| ground_vehicle \| hoop_earrings \| mismatched_footwear \| mismatched_legwear \| motor_vehicle \| nail_polish \| official_alternate_costume \| tube_top \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-----------\|:-------------\|:--------------------\|:-------\|:-----------------\|:-----------\|:---------------\|:-----------------------\|:----------\|:---------------\|:-------------\|:--------\|:-----------\|:--------\|:--------------\|:-------------------\|:--------------------\|:-------------\|:-------------\|:---------\|:--------\|:-------------\|:--------------\|:----------\|:--------------\|:-----------------\|:----------------\|:-------------\|:-----\|:--------------\|:--------\|:----------\|:---------\|:-----------------\|:-----------\|:---------\|:-----------\|:------------------\|:-----------------------\|:--------\|:-------------\|:-------------\|:---------------\|:---------------\|:-----------\|:-----------------\|:--------\|:---------\|:---------\|:-------------\|:----------\|:----------\|:-----------------\|:-----------------\|:-----------\|:----------\|:-----------\|:------\|:----------\|:----------\|:--------\|:----------\|:-------------------\|:--------------\|:-----------\|:-----------------------\|:--------------\|:---------------\|:---------\|:-----------\|:---------\|:---------------\|:-------------\|:----------------\|:------\|:---------------\|:----------------\|:------------\|:--------------------\|:--------------\|:---------------\|:---------\|:---------\|:-------------\|:--------------\|:-------------\|:--------------\|:-------------------\|:-------\|:--------------\|:----------------\|:------------\|:-----------\|:--------------\|:-----------------\|:------------\|:-----------------\|:----------------\|:----------------------\|:---------------------\|:----------------\|:--------------\|:-----------------------------\|:-----------\| \| 0 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 14 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 10 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| X \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
polinaeterna/old_push	--- dataset_info: features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 800 num_examples: 50 download_size: 1763 dataset_size: 800 --- # Dataset Card for "old_push" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-lener_br-lener_br-2a71c5-1777061680	--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: pierreguillou/ner-bert-base-cased-pt-lenerbr metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: validation col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: pierreguillou/ner-bert-base-cased-pt-lenerbr * Dataset: lener_br * Config: lener_br * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.
Orange/paraqa-sparqltotext	--- dataset_info: features: - name: uid dtype: string - name: query dtype: string - name: question dtype: string - name: simplified_query dtype: string - name: answer dtype: string - name: verbalized_answer dtype: string - name: verbalized_answer_2 dtype: string - name: verbalized_answer_3 dtype: string - name: verbalized_answer_4 dtype: string - name: verbalized_answer_5 dtype: string - name: verbalized_answer_6 dtype: string - name: verbalized_answer_7 dtype: string - name: verbalized_answer_8 dtype: string splits: - name: train num_bytes: 2540548 num_examples: 3500 - name: validation num_bytes: 369571 num_examples: 500 - name: test num_bytes: 722302 num_examples: 1000 download_size: 1750172 dataset_size: 3632421 task_categories: - conversational - question-answering - text-generation - text2text-generation tags: - qa - knowledge-graph - sparql --- # Dataset Card for ParaQA-SPARQLtoText ## Table of Contents - [Dataset Card for ParaQA-SPARQLtoText](#dataset-card-for-paraqa-sparqltotext) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [New field `simplified_query`](#new-field-simplified_query) - [New split "valid"](#new-split-valid) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Types of questions](#types-of-questions) - [Data splits](#data-splits) - [Additional information](#additional-information) - [Related datasets](#related-datasets) - [Licencing information](#licencing-information) - [Citation information](#citation-information) - [This version of the corpus (with normalized SPARQL queries)](#this-version-of-the-corpus-with-normalized-sparql-queries) - [Original version](#original-version) ## Dataset Description - Paper: [SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (AACL-IJCNLP 2022)](https://aclanthology.org/2022.aacl-main.11/) - Point of Contact: Gwénolé Lecorvé ### Dataset Summary Special version of ParaQA with SPARQL queries formatted for the SPARQL-to-Text task #### New field `simplified_query` New field is named "simplified_query". It results from applying the following step on the field "query": * Replacing URIs with a simpler format with prefix "resource:", "property:" and "ontology:". * Spacing the delimiters `(`, `{`, `.`, `}`, `)`. * Randomizing the variables names * Shuffling the clauses #### New split "valid" A validation set was randonly extracted from the test set to represent 10% of the whole dataset. ### Languages - English ## Dataset Structure ### Types of questions Comparison of question types compared to related datasets: \| \| \| [SimpleQuestions](https://huggingface.co/datasets/OrangeInnov/simplequestions-sparqltotext) \| [ParaQA](https://huggingface.co/datasets/OrangeInnov/paraqa-sparqltotext) \| [LC-QuAD 2.0](https://huggingface.co/datasets/OrangeInnov/lcquad_2.0-sparqltotext) \| [CSQA](https://huggingface.co/datasets/OrangeInnov/csqa-sparqltotext) \| [WebNLQ-QA](https://huggingface.co/datasets/OrangeInnov/webnlg-qa) \| \|--------------------------\|-----------------\|:---------------:\|:------:\|:-----------:\|:----:\|:---------:\| \| Number of triplets in query \| 1 \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| 2 \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| More \| \| \| ✓ \| ✓ \| ✓ \| \| Logical connector between triplets \| Conjunction \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Disjunction \| \| \| \| ✓ \| ✓ \| \| \| Exclusion \| \| \| \| ✓ \| ✓ \| \| Topology of the query graph \| Direct \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Sibling \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Chain \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Mixed \| \| \| ✓ \| \| ✓ \| \| \| Other \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| Variable typing in the query \| None \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Target variable \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Internal variable \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| Comparisons clauses \| None \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| String \| \| \| ✓ \| \| ✓ \| \| \| Number \| \| \| ✓ \| ✓ \| ✓ \| \| \| Date \| \| \| ✓ \| \| ✓ \| \| Superlative clauses \| No \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Yes \| \| \| \| ✓ \| \| \| Answer type \| Entity (open) \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Entity (closed) \| \| \| \| ✓ \| ✓ \| \| \| Number \| \| \| ✓ \| ✓ \| ✓ \| \| \| Boolean \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| Answer cardinality \| 0 (unanswerable) \| \| \| ✓ \| \| ✓ \| \| \| 1 \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| More \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| Number of target variables \| 0 (⇒ ASK verb) \| \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| 1 \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| 2 \| \| \| ✓ \| \| ✓ \| \| Dialogue context \| Self-sufficient \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Coreference \| \| \| \| ✓ \| ✓ \| \| \| Ellipsis \| \| \| \| ✓ \| ✓ \| \| Meaning \| Meaningful \| ✓ \| ✓ \| ✓ \| ✓ \| ✓ \| \| \| Non-sense \| \| \| \| \| ✓ \| ### Data splits Text verbalization is only available for a subset of the test set, referred to as challenge set. Other sample only contain dialogues in the form of follow-up sparql queries. \| \| Train \| Validation \| Test \| \| --------------------- \| ---------- \| ---------- \| ---------- \| \| Questions \| 3,500 \| 500 \| 1,000 \| \| NL question per query \| 1 \| \| Characters per query \| 103 (± 27) \| \| Tokens per question \| 10.3 (± 3.7) \| ## Additional information ### Related datasets This corpus is part of a set of 5 datasets released for SPARQL-to-Text generation, namely: - Non conversational datasets - [SimpleQuestions](https://huggingface.co/datasets/OrangeInnov/simplequestions-sparqltotext) (from https://github.com/askplatypus/wikidata-simplequestions) - [ParaQA](https://huggingface.co/datasets/OrangeInnov/paraqa-sparqltotext) (from https://github.com/barshana-banerjee/ParaQA) - [LC-QuAD 2.0](https://huggingface.co/datasets/OrangeInnov/lcquad_2.0-sparqltotext) (from http://lc-quad.sda.tech/) - Conversational datasets - [CSQA](https://huggingface.co/datasets/OrangeInnov/csqa-sparqltotext) (from https://amritasaha1812.github.io/CSQA/) - [WebNLQ-QA](https://huggingface.co/datasets/OrangeInnov/webnlg-qa) (derived from https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/release_v3.0) ### Licencing information * Content from original dataset: CC-BY 4.0 * New content: CC BY-SA 4.0 ### Citation information #### This version of the corpus (with normalized SPARQL queries) ```bibtex @inproceedings{lecorve2022sparql2text, title={SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications}, author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.}, journal={Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)}, year={2022} } ``` #### Original version ```bibtex @inproceedings{kacupaj2021paraqa, title={Paraqa: a question answering dataset with paraphrase responses for single-turn conversation}, author={Kacupaj, Endri and Banerjee, Barshana and Singh, Kuldeep and Lehmann, Jens}, booktitle={European semantic web conference}, pages={598--613}, year={2021}, organization={Springer} } ```
CyberHarem/sumi_otokawa_sakuratrick	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Sumi Otokawa This is the dataset of Sumi Otokawa, containing 49 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 49 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 117 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 145 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 49 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 49 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 49 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 117 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 117 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 101 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 145 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 145 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
open-llm-leaderboard/details_cognitivecomputations__MegaDolphin-120b	--- pretty_name: Evaluation run of cognitivecomputations/MegaDolphin-120b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cognitivecomputations/MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b)\ \ 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__MegaDolphin-120b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-21T21:48:58.549252](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__MegaDolphin-120b/blob/main/results_2024-01-21T21-48-58.549252.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\": 0.6902282983987049,\n\ \ \"acc_stderr\": 0.03061680900457877,\n \"acc_norm\": 0.6956826713561578,\n\ \ \"acc_norm_stderr\": 0.03120808276540501,\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.017240861812099804,\n \"mc2\": 0.592821117756168,\n\ \ \"mc2_stderr\": 0.015249093012153285\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620196,\n\ \ \"acc_norm\": 0.6902730375426621,\n \"acc_norm_stderr\": 0.013512058415238361\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7026488747261501,\n\ \ \"acc_stderr\": 0.004561582009834577,\n \"acc_norm\": 0.8780123481378211,\n\ \ \"acc_norm_stderr\": 0.0032660269509226444\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\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7828947368421053,\n \"acc_stderr\": 0.03355045304882923,\n\ \ \"acc_norm\": 0.7828947368421053,\n \"acc_norm_stderr\": 0.03355045304882923\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.78,\n\ \ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \ \ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.0271342916287417,\n\ \ \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.0271342916287417\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8194444444444444,\n\ \ \"acc_stderr\": 0.03216600808802268,\n \"acc_norm\": 0.8194444444444444,\n\ \ \"acc_norm_stderr\": 0.03216600808802268\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\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\": 0.6936416184971098,\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\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062946,\n\ \ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062946\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6893617021276596,\n \"acc_stderr\": 0.03025123757921317,\n\ \ \"acc_norm\": 0.6893617021276596,\n \"acc_norm_stderr\": 0.03025123757921317\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6620689655172414,\n \"acc_stderr\": 0.039417076320648906,\n\ \ \"acc_norm\": 0.6620689655172414,\n \"acc_norm_stderr\": 0.039417076320648906\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.47619047619047616,\n \"acc_stderr\": 0.025722097064388525,\n \"\ acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.025722097064388525\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5238095238095238,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8290322580645161,\n \"acc_stderr\": 0.02141724293632158,\n \"\ acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.02141724293632158\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\"\ : 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781678,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781678\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8585858585858586,\n \"acc_stderr\": 0.02482590979334335,\n \"\ acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.02482590979334335\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7461538461538462,\n \"acc_stderr\": 0.022066054378726257,\n\ \ \"acc_norm\": 0.7461538461538462,\n \"acc_norm_stderr\": 0.022066054378726257\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606647,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606647\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295894,\n \ \ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295894\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.4370860927152318,\n \"acc_stderr\": 0.04050035722230636,\n \"\ acc_norm\": 0.4370860927152318,\n \"acc_norm_stderr\": 0.04050035722230636\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8844036697247707,\n \"acc_stderr\": 0.01370874953417264,\n \"\ acc_norm\": 0.8844036697247707,\n \"acc_norm_stderr\": 0.01370874953417264\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"\ acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073315,\n \"\ acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073315\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.890295358649789,\n \"acc_stderr\": 0.020343400734868834,\n \ \ \"acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.020343400734868834\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7847533632286996,\n\ \ \"acc_stderr\": 0.02758406660220827,\n \"acc_norm\": 0.7847533632286996,\n\ \ \"acc_norm_stderr\": 0.02758406660220827\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622814,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622814\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\": 0.8466257668711656,\n \"acc_stderr\": 0.028311601441438607,\n\ \ \"acc_norm\": 0.8466257668711656,\n \"acc_norm_stderr\": 0.028311601441438607\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5714285714285714,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.5714285714285714,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822582,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822582\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.905982905982906,\n\ \ \"acc_stderr\": 0.019119892798924974,\n \"acc_norm\": 0.905982905982906,\n\ \ \"acc_norm_stderr\": 0.019119892798924974\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8441890166028098,\n\ \ \"acc_stderr\": 0.012969269247762578,\n \"acc_norm\": 0.8441890166028098,\n\ \ \"acc_norm_stderr\": 0.012969269247762578\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7832369942196532,\n \"acc_stderr\": 0.022183477668412856,\n\ \ \"acc_norm\": 0.7832369942196532,\n \"acc_norm_stderr\": 0.022183477668412856\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.5150837988826815,\n\ \ \"acc_stderr\": 0.01671489037999606,\n \"acc_norm\": 0.5150837988826815,\n\ \ \"acc_norm_stderr\": 0.01671489037999606\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7684887459807074,\n\ \ \"acc_stderr\": 0.023956532766639133,\n \"acc_norm\": 0.7684887459807074,\n\ \ \"acc_norm_stderr\": 0.023956532766639133\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.02161380939522479,\n\ \ \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.02161380939522479\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5567375886524822,\n \"acc_stderr\": 0.029634838473766006,\n \ \ \"acc_norm\": 0.5567375886524822,\n \"acc_norm_stderr\": 0.029634838473766006\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5867014341590613,\n\ \ \"acc_stderr\": 0.012576779494860076,\n \"acc_norm\": 0.5867014341590613,\n\ \ \"acc_norm_stderr\": 0.012576779494860076\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7095588235294118,\n \"acc_stderr\": 0.027576468622740533,\n\ \ \"acc_norm\": 0.7095588235294118,\n \"acc_norm_stderr\": 0.027576468622740533\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7565359477124183,\n \"acc_stderr\": 0.017362473762146606,\n \ \ \"acc_norm\": 0.7565359477124183,\n \"acc_norm_stderr\": 0.017362473762146606\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7714285714285715,\n \"acc_stderr\": 0.02688214492230774,\n\ \ \"acc_norm\": 0.7714285714285715,\n \"acc_norm_stderr\": 0.02688214492230774\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306046,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306046\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\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\": 0.027966785859160896,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160896\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.017240861812099804,\n \"mc2\": 0.592821117756168,\n\ \ \"mc2_stderr\": 0.015249093012153285\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8184688239936859,\n \"acc_stderr\": 0.010833276515007508\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.4624715693707354,\n \ \ \"acc_stderr\": 0.013733636059107756\n }\n}\n```" repo_url: https://huggingface.co/cognitivecomputations/MegaDolphin-120b 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_21T21_48_58.549252 path: - '/details_harness\|arc:challenge\|25_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-21T21-48-58.549252.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|gsm8k\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hellaswag\|10_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-21T21-48-58.549252.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-21T21-48-58.549252.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '/details_harness\|winogrande\|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-21T21-48-58.549252.parquet' - config_name: results data_files: - split: 2024_01_21T21_48_58.549252 path: - results_2024-01-21T21-48-58.549252.parquet - split: latest path: - results_2024-01-21T21-48-58.549252.parquet --- # Dataset Card for Evaluation run of cognitivecomputations/MegaDolphin-120b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cognitivecomputations/MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b) 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__MegaDolphin-120b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-21T21:48:58.549252](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__MegaDolphin-120b/blob/main/results_2024-01-21T21-48-58.549252.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": 0.6902282983987049, "acc_stderr": 0.03061680900457877, "acc_norm": 0.6956826713561578, "acc_norm_stderr": 0.03120808276540501, "mc1": 0.41370869033047736, "mc1_stderr": 0.017240861812099804, "mc2": 0.592821117756168, "mc2_stderr": 0.015249093012153285 }, "harness\|arc:challenge\|25": { "acc": 0.643344709897611, "acc_stderr": 0.013998056902620196, "acc_norm": 0.6902730375426621, "acc_norm_stderr": 0.013512058415238361 }, "harness\|hellaswag\|10": { "acc": 0.7026488747261501, "acc_stderr": 0.004561582009834577, "acc_norm": 0.8780123481378211, "acc_norm_stderr": 0.0032660269509226444 }, "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": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7828947368421053, "acc_stderr": 0.03355045304882923, "acc_norm": 0.7828947368421053, "acc_norm_stderr": 0.03355045304882923 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7358490566037735, "acc_stderr": 0.0271342916287417, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.0271342916287417 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8194444444444444, "acc_stderr": 0.03216600808802268, "acc_norm": 0.8194444444444444, "acc_norm_stderr": 0.03216600808802268 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "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": 0.6936416184971098, "acc_stderr": 0.035149425512674394, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.035149425512674394 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.35294117647058826, "acc_stderr": 0.04755129616062946, "acc_norm": 0.35294117647058826, "acc_norm_stderr": 0.04755129616062946 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816507, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816507 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6893617021276596, "acc_stderr": 0.03025123757921317, "acc_norm": 0.6893617021276596, "acc_norm_stderr": 0.03025123757921317 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6620689655172414, "acc_stderr": 0.039417076320648906, "acc_norm": 0.6620689655172414, "acc_norm_stderr": 0.039417076320648906 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.025722097064388525, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.025722097064388525 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5238095238095238, "acc_stderr": 0.04467062628403273, "acc_norm": 0.5238095238095238, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8290322580645161, "acc_stderr": 0.02141724293632158, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 0.02141724293632158 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5024630541871922, "acc_stderr": 0.03517945038691063, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781678, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781678 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8585858585858586, "acc_stderr": 0.02482590979334335, "acc_norm": 0.8585858585858586, "acc_norm_stderr": 0.02482590979334335 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7461538461538462, "acc_stderr": 0.022066054378726257, "acc_norm": 0.7461538461538462, "acc_norm_stderr": 0.022066054378726257 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3148148148148148, "acc_stderr": 0.02831753349606647, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.02831753349606647 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7815126050420168, "acc_stderr": 0.02684151432295894, "acc_norm": 0.7815126050420168, "acc_norm_stderr": 0.02684151432295894 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4370860927152318, "acc_stderr": 0.04050035722230636, "acc_norm": 0.4370860927152318, "acc_norm_stderr": 0.04050035722230636 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8844036697247707, "acc_stderr": 0.01370874953417264, "acc_norm": 0.8844036697247707, "acc_norm_stderr": 0.01370874953417264 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5787037037037037, "acc_stderr": 0.03367462138896078, "acc_norm": 0.5787037037037037, "acc_norm_stderr": 0.03367462138896078 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9313725490196079, "acc_stderr": 0.017744453647073315, "acc_norm": 0.9313725490196079, "acc_norm_stderr": 0.017744453647073315 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.890295358649789, "acc_stderr": 0.020343400734868834, "acc_norm": 0.890295358649789, "acc_norm_stderr": 0.020343400734868834 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7847533632286996, "acc_stderr": 0.02758406660220827, "acc_norm": 0.7847533632286996, "acc_norm_stderr": 0.02758406660220827 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494733, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494733 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622814, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622814 }, "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": 0.8466257668711656, "acc_stderr": 0.028311601441438607, "acc_norm": 0.8466257668711656, "acc_norm_stderr": 0.028311601441438607 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5714285714285714, "acc_stderr": 0.04697113923010212, "acc_norm": 0.5714285714285714, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822582, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822582 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.905982905982906, "acc_stderr": 0.019119892798924974, "acc_norm": 0.905982905982906, "acc_norm_stderr": 0.019119892798924974 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252606, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8441890166028098, "acc_stderr": 0.012969269247762578, "acc_norm": 0.8441890166028098, "acc_norm_stderr": 0.012969269247762578 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7832369942196532, "acc_stderr": 0.022183477668412856, "acc_norm": 0.7832369942196532, "acc_norm_stderr": 0.022183477668412856 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.5150837988826815, "acc_stderr": 0.01671489037999606, "acc_norm": 0.5150837988826815, "acc_norm_stderr": 0.01671489037999606 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7684887459807074, "acc_stderr": 0.023956532766639133, "acc_norm": 0.7684887459807074, "acc_norm_stderr": 0.023956532766639133 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.02161380939522479, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.02161380939522479 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5567375886524822, "acc_stderr": 0.029634838473766006, "acc_norm": 0.5567375886524822, "acc_norm_stderr": 0.029634838473766006 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5867014341590613, "acc_stderr": 0.012576779494860076, "acc_norm": 0.5867014341590613, "acc_norm_stderr": 0.012576779494860076 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7095588235294118, "acc_stderr": 0.027576468622740533, "acc_norm": 0.7095588235294118, "acc_norm_stderr": 0.027576468622740533 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7565359477124183, "acc_stderr": 0.017362473762146606, "acc_norm": 0.7565359477124183, "acc_norm_stderr": 0.017362473762146606 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7714285714285715, "acc_stderr": 0.02688214492230774, "acc_norm": 0.7714285714285715, "acc_norm_stderr": 0.02688214492230774 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306046, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306046 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "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": 0.027966785859160896, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160896 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.41370869033047736, "mc1_stderr": 0.017240861812099804, "mc2": 0.592821117756168, "mc2_stderr": 0.015249093012153285 }, "harness\|winogrande\|5": { "acc": 0.8184688239936859, "acc_stderr": 0.010833276515007508 }, "harness\|gsm8k\|5": { "acc": 0.4624715693707354, "acc_stderr": 0.013733636059107756 } } ``` ## 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 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Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). 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PanoEvJ/GenAI-sample	--- dataset_info: features: - name: name dtype: string - name: description dtype: string - name: ad dtype: string splits: - name: train num_bytes: 3211 num_examples: 5 download_size: 0 dataset_size: 3211 --- # Dataset Card for "GenAI-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lalanikarim/glaive-function-calling-v2	--- language: - en license: apache-2.0 dataset_info: features: - name: chat dtype: string - name: system dtype: string - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 157115139.42965484 num_examples: 71678 - name: test num_bytes: 17458942.570345167 num_examples: 7965 download_size: 47435441 dataset_size: 174574082.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
claudiostu/voz1	--- license: openrail ---
ControlNet/LAV-DF	--- language: - en tags: - deepfakes - video pretty_name: LAV-DF task_categories: - video-classification paperswithcode_id: lav-df license: cc size_categories: - 100K<n<1M extra_gated_heading: Access LAV-DF dataset on Hugging Face extra_gated_prompt: To use this LAV-DF dataset, you should agree the [terms and conditions](https://github.com/ControlNet/LAV-DF/blob/master/TERMS_AND_CONDITIONS.md). --- # Localized Audio Visual DeepFake Dataset (LAV-DF) This repo is the dataset for the DICTA paper [Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization](https://ieeexplore.ieee.org/document/10034605) (Best Award), and the journal paper ["Glitch in the Matrix!": A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization](https://arxiv.org/abs/2305.01979) submitted to CVIU. ## LAV-DF Dataset ### Download To use this LAV-DF dataset, you should agree the [terms and conditions](https://github.com/ControlNet/LAV-DF/blob/master/TERMS_AND_CONDITIONS.md). Download link: [Google Drive](https://drive.google.com/file/d/1-OQ-NDtdEyqHNLaZU1Lt9Upk5wVqfYJw/view?usp=sharing), or [HuggingFace](https://huggingface.co/datasets/ControlNet/LAV-DF). ## License This project is under the CC BY-NC 4.0 license. See [LICENSE](https://github.com/ControlNet/LAV-DF/blob/master/LICENSE) for details. ## References If you find this work useful in your research, please cite them. The conference paper, ```bibtex @inproceedings{cai2022you, title = {Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization}, author = {Cai, Zhixi and Stefanov, Kalin and Dhall, Abhinav and Hayat, Munawar}, booktitle = {2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)}, year = {2022}, doi = {10.1109/DICTA56598.2022.10034605}, pages = {1--10}, address = {Sydney, Australia}, } ``` The extended journal version currently under review at CVIU, ```bibtex @article{cai2023glitch, title = {Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization}, author = {Cai, Zhixi and Ghosh, Shreya and Dhall, Abhinav and Gedeon, Tom and Stefanov, Kalin and Hayat, Munawar}, journal = {arXiv preprint arXiv:2305.01979}, year = {2023}, } ```
HiTZ/casimedicos-squad	--- license: cc-by-4.0 language: - es tags: - casimedicos - explainability - medical exams - medical question answering - extractive question answering - squad - multilinguality - LLMs - LLM pretty_name: casimedicos-squad configs: - config_name: es data_files: - split: train path: - data/es/es_train_casimedicos_squad.json - split: validation path: - data/es/es_dev_casimedicos_squad.json - split: test path: - data/es/es_test_casimedicos_squad.json task_categories: - question-answering size_categories: - 1K<n<10K --- <p align="center"> <br> <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 200px;"> <br> # Antidote CasiMedicos in SQuAD Format for Explanatory Argument Extraction We present a new multilingual parallel medical dataset of commented medical exams which includes not only explanatory arguments for the correct answer but also arguments to explain why the remaining possible answers are incorrect. Furthermore, this dataset allows us to setup a novel extractive task which consists of identifying the explanation of the correct answer written by medical doctors. In order to do so we leverage the SQuAD extractive QA paradigm to automatically evaluate performance of language models to identify the explanation of the correct answer in medical exams without relying on costly manual evaluation by medical experts. The data source consists of Resident Medical Intern or Médico Interno Residente (MIR) exams, originally created by [CasiMedicos](https://www.casimedicos.com), a Spanish community of medical professionals who collaboratively, voluntarily, and free of charge, publishes written explanations about the possible answers included in the MIR exams. The aim is to generate a resource that helps future medical doctors to study towards the MIR examinations. The commented MIR exams, including the explanations, are published in the [CasiMedicos Project MIR 2.0 website](https://www.casimedicos.com/mir-2-0/). We have extracted, clean, structure and annotated the available data so that each document in casimedicos-squad includes the clinical case, the correct answer, the multiple-choice questions and the commented exam written by native Spanish medical doctors. The comments have been annotated with the span in the text that corresponds to the explanation of the correct answer (see example below). <table style="width:33%"> <tr> <th>casimedicos-squad splits</th> <tr> <td>train</td> <td>404</td> </tr> <tr> <td>validation</td> <td>56</td> </tr> <tr> <td>test</td> <td>119</td> </tr> </table> - 📖 Paper:[Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://arxiv.org/abs/2312.00567) - 💻 Github Repo (Data and Code): [https://github.com/ixa-ehu/antidote-casimedicos](https://github.com/ixa-ehu/antidote-casimedicos) - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote) - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR ## Example <p align="center"> <img src="https://github.com/ixa-ehu/antidote-casimedicos/blob/main/casimedicos-exp.png?raw=true" style="height: 650px;"> </p> The example above shows a document in CasiMedicos containing the textual content, including Clinical Case (C), Question (Q), Possible Answers (P), and Explanation (E). Furthermore, for casimedicos-squad we annotated the span in the explanation (E) that corresponds to the correct answer (A). The process of manually annotating the corpus consisted of specifying where the explanations of the correct answers begin and end. In order to obtain grammatically complete correct answer explanations, annotating full sentences or subordinate clauses was preferred over shorter spans. ## Data Explanation The dataset is structured as a list of documents ("paragraphs") where each of them include: - context: the explanation (E) in the document - qas: list of possible answers and questions. This element contains: - answers: an answer which corresponds to the explanation of the correct answer (A) - question: the clinical case (C) and question (Q) - id: unique identifier for the document ## Citation If you use this data please cite the following paper: ```bibtex @misc{goenaga2023explanatory, title={Explanatory Argument Extraction of Correct Answers in Resident Medical Exams}, author={Iakes Goenaga and Aitziber Atutxa and Koldo Gojenola and Maite Oronoz and Rodrigo Agerri}, year={2023}, eprint={2312.00567}, archivePrefix={arXiv} } ``` Contact: [Iakes Goenaga](http://www.hitz.eus/es/node/65) and [Rodrigo Agerri](https://ragerri.github.io/) HiTZ Center - Ixa, University of the Basque Country UPV/EHU
NobodyExistsOnTheInternet/ConvoOrcaShareGPT4096	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: evolutions sequence: string - name: textversion dtype: string - name: tokens dtype: int64 - name: too_long dtype: bool splits: - name: train num_bytes: 1808371367 num_examples: 46563 download_size: 882548173 dataset_size: 1808371367 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_jondurbin__bagel-34b-v0.2	--- pretty_name: Evaluation run of jondurbin/bagel-34b-v0.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jondurbin/bagel-34b-v0.2](https://huggingface.co/jondurbin/bagel-34b-v0.2) 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_jondurbin__bagel-34b-v0.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-05T02:46:07.466495](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__bagel-34b-v0.2/blob/main/results_2024-01-05T02-46-07.466495.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\": 0.7566584880323868,\n\ \ \"acc_stderr\": 0.028404446518444006,\n \"acc_norm\": 0.7644443889276894,\n\ \ \"acc_norm_stderr\": 0.028932547734181486,\n \"mc1\": 0.4369645042839657,\n\ \ \"mc1_stderr\": 0.017363844503195985,\n \"mc2\": 0.592598246243346,\n\ \ \"mc2_stderr\": 0.014870176336077599\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.64419795221843,\n \"acc_stderr\": 0.01399057113791876,\n\ \ \"acc_norm\": 0.6877133105802048,\n \"acc_norm_stderr\": 0.013542598541688065\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6347341167098187,\n\ \ \"acc_stderr\": 0.004805205798724566,\n \"acc_norm\": 0.8371838279227246,\n\ \ \"acc_norm_stderr\": 0.0036844333238877946\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.868421052631579,\n \"acc_stderr\": 0.027508689533549915,\n\ \ \"acc_norm\": 0.868421052631579,\n \"acc_norm_stderr\": 0.027508689533549915\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\": 0.8,\n \"acc_stderr\": 0.024618298195866514,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.024618298195866514\n \ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8958333333333334,\n\ \ \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n\ \ \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562429,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562429\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7514450867052023,\n\ \ \"acc_stderr\": 0.03295304696818317,\n \"acc_norm\": 0.7514450867052023,\n\ \ \"acc_norm_stderr\": 0.03295304696818317\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.049406356306056595\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\": 0.7914893617021277,\n \"acc_stderr\": 0.026556982117838742,\n\ \ \"acc_norm\": 0.7914893617021277,\n \"acc_norm_stderr\": 0.026556982117838742\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5614035087719298,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7655172413793103,\n \"acc_stderr\": 0.035306258743465914,\n\ \ \"acc_norm\": 0.7655172413793103,\n \"acc_norm_stderr\": 0.035306258743465914\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.7037037037037037,\n \"acc_stderr\": 0.023517294335963286,\n \"\ acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.023517294335963286\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.6031746031746031,\n\ \ \"acc_stderr\": 0.043758884927270585,\n \"acc_norm\": 0.6031746031746031,\n\ \ \"acc_norm_stderr\": 0.043758884927270585\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8903225806451613,\n\ \ \"acc_stderr\": 0.01777677870048519,\n \"acc_norm\": 0.8903225806451613,\n\ \ \"acc_norm_stderr\": 0.01777677870048519\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.645320197044335,\n \"acc_stderr\": 0.03366124489051449,\n\ \ \"acc_norm\": 0.645320197044335,\n \"acc_norm_stderr\": 0.03366124489051449\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\"\ : 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295138,\n\ \ \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295138\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.8179487179487179,\n \"acc_stderr\": 0.019565236782930893,\n\ \ \"acc_norm\": 0.8179487179487179,\n \"acc_norm_stderr\": 0.019565236782930893\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.43333333333333335,\n \"acc_stderr\": 0.030213340289237924,\n \ \ \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.030213340289237924\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8403361344537815,\n \"acc_stderr\": 0.023793353997528802,\n\ \ \"acc_norm\": 0.8403361344537815,\n \"acc_norm_stderr\": 0.023793353997528802\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\"\ : 0.9155963302752294,\n \"acc_stderr\": 0.011918819327334872,\n \"\ acc_norm\": 0.9155963302752294,\n \"acc_norm_stderr\": 0.011918819327334872\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6620370370370371,\n \"acc_stderr\": 0.03225941352631295,\n \"\ acc_norm\": 0.6620370370370371,\n \"acc_norm_stderr\": 0.03225941352631295\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813905,\n \"\ acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813905\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.9071729957805907,\n \"acc_stderr\": 0.01888975055095671,\n \ \ \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.01888975055095671\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8071748878923767,\n\ \ \"acc_stderr\": 0.026478240960489365,\n \"acc_norm\": 0.8071748878923767,\n\ \ \"acc_norm_stderr\": 0.026478240960489365\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.028718776889342327,\n\ \ \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.028718776889342327\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.0291998024556228,\n \"acc_norm\"\ : 0.8842975206611571,\n \"acc_norm_stderr\": 0.0291998024556228\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.030381596756651655,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.030381596756651655\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n\ \ \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5803571428571429,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.5803571428571429,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8932038834951457,\n \"acc_stderr\": 0.030581088928331356,\n\ \ \"acc_norm\": 0.8932038834951457,\n \"acc_norm_stderr\": 0.030581088928331356\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.015537514263253869,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.015537514263253869\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9067688378033205,\n\ \ \"acc_stderr\": 0.010397417087292847,\n \"acc_norm\": 0.9067688378033205,\n\ \ \"acc_norm_stderr\": 0.010397417087292847\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8265895953757225,\n \"acc_stderr\": 0.020383229551135022,\n\ \ \"acc_norm\": 0.8265895953757225,\n \"acc_norm_stderr\": 0.020383229551135022\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.8,\n\ \ \"acc_stderr\": 0.013378001241813075,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.013378001241813075\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8398692810457516,\n \"acc_stderr\": 0.020998740930362303,\n\ \ \"acc_norm\": 0.8398692810457516,\n \"acc_norm_stderr\": 0.020998740930362303\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8038585209003215,\n\ \ \"acc_stderr\": 0.022552447780478033,\n \"acc_norm\": 0.8038585209003215,\n\ \ \"acc_norm_stderr\": 0.022552447780478033\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8580246913580247,\n \"acc_stderr\": 0.019420260109438293,\n\ \ \"acc_norm\": 0.8580246913580247,\n \"acc_norm_stderr\": 0.019420260109438293\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6276595744680851,\n \"acc_stderr\": 0.02883892147125145,\n \ \ \"acc_norm\": 0.6276595744680851,\n \"acc_norm_stderr\": 0.02883892147125145\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5847457627118644,\n\ \ \"acc_stderr\": 0.012585471793400664,\n \"acc_norm\": 0.5847457627118644,\n\ \ \"acc_norm_stderr\": 0.012585471793400664\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8051470588235294,\n \"acc_stderr\": 0.02406059942348742,\n\ \ \"acc_norm\": 0.8051470588235294,\n \"acc_norm_stderr\": 0.02406059942348742\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8104575163398693,\n \"acc_stderr\": 0.015856152189980256,\n \ \ \"acc_norm\": 0.8104575163398693,\n \"acc_norm_stderr\": 0.015856152189980256\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\": 0.8326530612244898,\n \"acc_stderr\": 0.02389714476891452,\n\ \ \"acc_norm\": 0.8326530612244898,\n \"acc_norm_stderr\": 0.02389714476891452\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.9104477611940298,\n\ \ \"acc_stderr\": 0.02019067053502791,\n \"acc_norm\": 0.9104477611940298,\n\ \ \"acc_norm_stderr\": 0.02019067053502791\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072864,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072864\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4369645042839657,\n\ \ \"mc1_stderr\": 0.017363844503195985,\n \"mc2\": 0.592598246243346,\n\ \ \"mc2_stderr\": 0.014870176336077599\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8382004735595896,\n \"acc_stderr\": 0.010350128010292406\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.46171341925701287,\n \ \ \"acc_stderr\": 0.013732048227016682\n }\n}\n```" repo_url: https://huggingface.co/jondurbin/bagel-34b-v0.2 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_05T02_46_07.466495 path: - '/details_harness\|arc:challenge\|25_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-05T02-46-07.466495.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|gsm8k\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hellaswag\|10_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-05T02-46-07.466495.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-05T02-46-07.466495.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '/details_harness\|winogrande\|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-05T02-46-07.466495.parquet' - config_name: results data_files: - split: 2024_01_05T02_46_07.466495 path: - results_2024-01-05T02-46-07.466495.parquet - split: latest path: - results_2024-01-05T02-46-07.466495.parquet --- # Dataset Card for Evaluation run of jondurbin/bagel-34b-v0.2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jondurbin/bagel-34b-v0.2](https://huggingface.co/jondurbin/bagel-34b-v0.2) 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_jondurbin__bagel-34b-v0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-05T02:46:07.466495](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__bagel-34b-v0.2/blob/main/results_2024-01-05T02-46-07.466495.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": 0.7566584880323868, "acc_stderr": 0.028404446518444006, "acc_norm": 0.7644443889276894, "acc_norm_stderr": 0.028932547734181486, "mc1": 0.4369645042839657, "mc1_stderr": 0.017363844503195985, "mc2": 0.592598246243346, "mc2_stderr": 0.014870176336077599 }, "harness\|arc:challenge\|25": { "acc": 0.64419795221843, "acc_stderr": 0.01399057113791876, "acc_norm": 0.6877133105802048, "acc_norm_stderr": 0.013542598541688065 }, "harness\|hellaswag\|10": { "acc": 0.6347341167098187, "acc_stderr": 0.004805205798724566, "acc_norm": 0.8371838279227246, "acc_norm_stderr": 0.0036844333238877946 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.868421052631579, "acc_stderr": 0.027508689533549915, "acc_norm": 0.868421052631579, "acc_norm_stderr": 0.027508689533549915 }, "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": 0.8, "acc_stderr": 0.024618298195866514, "acc_norm": 0.8, "acc_norm_stderr": 0.024618298195866514 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145633, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.43, "acc_stderr": 0.04975698519562429, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562429 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7514450867052023, "acc_stderr": 0.03295304696818317, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.03295304696818317 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5588235294117647, "acc_stderr": 0.049406356306056595, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.049406356306056595 }, "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": 0.7914893617021277, "acc_stderr": 0.026556982117838742, "acc_norm": 0.7914893617021277, "acc_norm_stderr": 0.026556982117838742 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5614035087719298, "acc_stderr": 0.04668000738510455, "acc_norm": 0.5614035087719298, "acc_norm_stderr": 0.04668000738510455 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7655172413793103, "acc_stderr": 0.035306258743465914, "acc_norm": 0.7655172413793103, "acc_norm_stderr": 0.035306258743465914 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.7037037037037037, "acc_stderr": 0.023517294335963286, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.023517294335963286 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.6031746031746031, "acc_stderr": 0.043758884927270585, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.043758884927270585 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8903225806451613, "acc_stderr": 0.01777677870048519, "acc_norm": 0.8903225806451613, "acc_norm_stderr": 0.01777677870048519 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.645320197044335, "acc_stderr": 0.03366124489051449, "acc_norm": 0.645320197044335, "acc_norm_stderr": 0.03366124489051449 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036845, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9637305699481865, "acc_stderr": 0.013492659751295138, "acc_norm": 0.9637305699481865, "acc_norm_stderr": 0.013492659751295138 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8179487179487179, "acc_stderr": 0.019565236782930893, "acc_norm": 0.8179487179487179, "acc_norm_stderr": 0.019565236782930893 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.43333333333333335, "acc_stderr": 0.030213340289237924, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.030213340289237924 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8403361344537815, "acc_stderr": 0.023793353997528802, "acc_norm": 0.8403361344537815, "acc_norm_stderr": 0.023793353997528802 }, "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": 0.9155963302752294, "acc_stderr": 0.011918819327334872, "acc_norm": 0.9155963302752294, "acc_norm_stderr": 0.011918819327334872 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6620370370370371, "acc_stderr": 0.03225941352631295, "acc_norm": 0.6620370370370371, "acc_norm_stderr": 0.03225941352631295 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9166666666666666, "acc_stderr": 0.019398452135813905, "acc_norm": 0.9166666666666666, "acc_norm_stderr": 0.019398452135813905 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9071729957805907, "acc_stderr": 0.01888975055095671, "acc_norm": 0.9071729957805907, "acc_norm_stderr": 0.01888975055095671 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8071748878923767, "acc_stderr": 0.026478240960489365, "acc_norm": 0.8071748878923767, "acc_norm_stderr": 0.026478240960489365 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8778625954198473, "acc_stderr": 0.028718776889342327, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.028718776889342327 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8842975206611571, "acc_stderr": 0.0291998024556228, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.0291998024556228 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.030381596756651655, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.030381596756651655 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5803571428571429, "acc_stderr": 0.04684099321077106, "acc_norm": 0.5803571428571429, "acc_norm_stderr": 0.04684099321077106 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8932038834951457, "acc_stderr": 0.030581088928331356, "acc_norm": 0.8932038834951457, "acc_norm_stderr": 0.030581088928331356 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253869, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253869 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9067688378033205, "acc_stderr": 0.010397417087292847, "acc_norm": 0.9067688378033205, "acc_norm_stderr": 0.010397417087292847 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8265895953757225, "acc_stderr": 0.020383229551135022, "acc_norm": 0.8265895953757225, "acc_norm_stderr": 0.020383229551135022 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.8, "acc_stderr": 0.013378001241813075, "acc_norm": 0.8, "acc_norm_stderr": 0.013378001241813075 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8398692810457516, "acc_stderr": 0.020998740930362303, "acc_norm": 0.8398692810457516, "acc_norm_stderr": 0.020998740930362303 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8038585209003215, "acc_stderr": 0.022552447780478033, "acc_norm": 0.8038585209003215, "acc_norm_stderr": 0.022552447780478033 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8580246913580247, "acc_stderr": 0.019420260109438293, "acc_norm": 0.8580246913580247, "acc_norm_stderr": 0.019420260109438293 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6276595744680851, "acc_stderr": 0.02883892147125145, "acc_norm": 0.6276595744680851, "acc_norm_stderr": 0.02883892147125145 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5847457627118644, "acc_stderr": 0.012585471793400664, "acc_norm": 0.5847457627118644, "acc_norm_stderr": 0.012585471793400664 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8051470588235294, "acc_stderr": 0.02406059942348742, "acc_norm": 0.8051470588235294, "acc_norm_stderr": 0.02406059942348742 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8104575163398693, "acc_stderr": 0.015856152189980256, "acc_norm": 0.8104575163398693, "acc_norm_stderr": 0.015856152189980256 }, "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": 0.8326530612244898, "acc_stderr": 0.02389714476891452, "acc_norm": 0.8326530612244898, "acc_norm_stderr": 0.02389714476891452 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.9104477611940298, "acc_stderr": 0.02019067053502791, "acc_norm": 0.9104477611940298, "acc_norm_stderr": 0.02019067053502791 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072864, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072864 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4369645042839657, "mc1_stderr": 0.017363844503195985, "mc2": 0.592598246243346, "mc2_stderr": 0.014870176336077599 }, "harness\|winogrande\|5": { "acc": 0.8382004735595896, "acc_stderr": 0.010350128010292406 }, "harness\|gsm8k\|5": { "acc": 0.46171341925701287, "acc_stderr": 0.013732048227016682 } } ``` ## 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]
rafatecno1/rafa	--- license: openrail ---
alvations/c4p0-x1-en-fr	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 4047 num_examples: 2 download_size: 21379 dataset_size: 4047 configs: - config_name: default data_files: - split: train path: 37ded8bbdbf8c054/train-* ---
pib	--- task_categories: - translation - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling multilinguality: - translation language: - bn - en - gu - hi - ml - mr - or - pa - ta - te - ur language_creators: - other annotations_creators: - no-annotation source_datasets: - original size_categories: - 100K<n<1M - 10K<n<100K license: - cc-by-4.0 paperswithcode_id: null pretty_name: CVIT PIB dataset_info: - config_name: or-ur features: - name: translation dtype: translation: languages: - or - ur splits: - name: train num_bytes: 27790211 num_examples: 43766 download_size: 393352875 dataset_size: 27790211 - config_name: ml-or features: - name: translation dtype: translation: languages: - ml - or splits: - name: train num_bytes: 16011549 num_examples: 19413 download_size: 393352875 dataset_size: 16011549 - config_name: bn-ta features: - name: translation dtype: translation: languages: - bn - ta splits: - name: train num_bytes: 28706668 num_examples: 33005 download_size: 393352875 dataset_size: 28706668 - config_name: gu-mr features: - name: translation dtype: translation: languages: - gu - mr splits: - name: train num_bytes: 24253770 num_examples: 30766 download_size: 393352875 dataset_size: 24253770 - config_name: hi-or features: - name: translation dtype: translation: languages: - hi - or splits: - name: train num_bytes: 45086618 num_examples: 61070 download_size: 393352875 dataset_size: 45086618 - config_name: en-or features: - name: translation dtype: translation: languages: - en - or splits: - name: train num_bytes: 51258494 num_examples: 98230 download_size: 393352875 dataset_size: 51258494 - config_name: mr-ur features: - name: translation dtype: translation: languages: - mr - ur splits: - name: train num_bytes: 34053295 num_examples: 49691 download_size: 393352875 dataset_size: 34053295 - config_name: en-ta features: - name: translation dtype: translation: languages: - en - ta splits: - name: train num_bytes: 74931542 num_examples: 118759 download_size: 393352875 dataset_size: 74931542 - config_name: hi-ta features: - name: translation dtype: translation: languages: - hi - ta splits: - name: train num_bytes: 57628429 num_examples: 64945 download_size: 393352875 dataset_size: 57628429 - config_name: bn-en features: - name: translation dtype: translation: languages: - bn - en splits: - name: train num_bytes: 53291968 num_examples: 93560 download_size: 393352875 dataset_size: 53291968 - config_name: bn-or features: - name: translation dtype: translation: languages: - bn - or splits: - name: train num_bytes: 19819136 num_examples: 26456 download_size: 393352875 dataset_size: 19819136 - config_name: ml-ta features: - name: translation dtype: translation: languages: - ml - ta splits: - name: train num_bytes: 21685938 num_examples: 23609 download_size: 393352875 dataset_size: 21685938 - config_name: gu-ur features: - name: translation dtype: translation: languages: - gu - ur splits: - name: train num_bytes: 20312414 num_examples: 29938 download_size: 393352875 dataset_size: 20312414 - config_name: bn-ml features: - name: translation dtype: translation: languages: - bn - ml splits: - name: train num_bytes: 15545271 num_examples: 18149 download_size: 393352875 dataset_size: 15545271 - config_name: ml-pa features: - name: translation dtype: translation: languages: - ml - pa splits: - name: train num_bytes: 18114904 num_examples: 21978 download_size: 393352875 dataset_size: 18114904 - config_name: en-pa features: - name: translation dtype: translation: languages: - en - pa splits: - name: train num_bytes: 56316514 num_examples: 103296 download_size: 393352875 dataset_size: 56316514 - config_name: bn-hi features: - name: translation dtype: translation: languages: - bn - hi splits: - name: train num_bytes: 40970170 num_examples: 49598 download_size: 393352875 dataset_size: 40970170 - config_name: hi-pa features: - name: translation dtype: translation: languages: - hi - pa splits: - name: train num_bytes: 59293062 num_examples: 75200 download_size: 393352875 dataset_size: 59293062 - config_name: gu-te features: - name: translation dtype: translation: languages: - gu - te splits: - name: train num_bytes: 14517828 num_examples: 16335 download_size: 393352875 dataset_size: 14517828 - config_name: pa-ta features: - name: translation dtype: translation: languages: - pa - ta splits: - name: train num_bytes: 39144065 num_examples: 46349 download_size: 393352875 dataset_size: 39144065 - config_name: hi-ml features: - name: translation dtype: translation: languages: - hi - ml splits: - name: train num_bytes: 24015298 num_examples: 27167 download_size: 393352875 dataset_size: 24015298 - config_name: or-te features: - name: translation dtype: translation: languages: - or - te splits: - name: train num_bytes: 9011734 num_examples: 10475 download_size: 393352875 dataset_size: 9011734 - config_name: en-ml features: - name: translation dtype: translation: languages: - en - ml splits: - name: train num_bytes: 27754969 num_examples: 44986 download_size: 393352875 dataset_size: 27754969 - config_name: en-hi features: - name: translation dtype: translation: languages: - en - hi splits: - name: train num_bytes: 160009440 num_examples: 269594 download_size: 393352875 dataset_size: 160009440 - config_name: bn-pa features: - name: translation dtype: translation: languages: - bn - pa splits: - name: train num_bytes: 27522373 num_examples: 35109 download_size: 393352875 dataset_size: 27522373 - config_name: mr-te features: - name: translation dtype: translation: languages: - mr - te splits: - name: train num_bytes: 16838115 num_examples: 18179 download_size: 393352875 dataset_size: 16838115 - config_name: mr-pa features: - name: translation dtype: translation: languages: - mr - pa splits: - name: train num_bytes: 38720410 num_examples: 50418 download_size: 393352875 dataset_size: 38720410 - config_name: bn-te features: - name: translation dtype: translation: languages: - bn - te splits: - name: train num_bytes: 15529843 num_examples: 17605 download_size: 393352875 dataset_size: 15529843 - config_name: gu-hi features: - name: translation dtype: translation: languages: - gu - hi splits: - name: train num_bytes: 33606230 num_examples: 41587 download_size: 393352875 dataset_size: 33606230 - config_name: ta-ur features: - name: translation dtype: translation: languages: - ta - ur splits: - name: train num_bytes: 37593813 num_examples: 48892 download_size: 393352875 dataset_size: 37593813 - config_name: te-ur features: - name: translation dtype: translation: languages: - te - ur splits: - name: train num_bytes: 16485209 num_examples: 21148 download_size: 393352875 dataset_size: 16485209 - config_name: or-pa features: - name: translation dtype: translation: languages: - or - pa splits: - name: train num_bytes: 30081903 num_examples: 43159 download_size: 393352875 dataset_size: 30081903 - config_name: gu-ml features: - name: translation dtype: translation: languages: - gu - ml splits: - name: train num_bytes: 15749821 num_examples: 18252 download_size: 393352875 dataset_size: 15749821 - config_name: gu-pa features: - name: translation dtype: translation: languages: - gu - pa splits: - name: train num_bytes: 27441041 num_examples: 35566 download_size: 393352875 dataset_size: 27441041 - config_name: hi-te features: - name: translation dtype: translation: languages: - hi - te splits: - name: train num_bytes: 26473814 num_examples: 28569 download_size: 393352875 dataset_size: 26473814 - config_name: en-te features: - name: translation dtype: translation: languages: - en - te splits: - name: train num_bytes: 28620219 num_examples: 44888 download_size: 393352875 dataset_size: 28620219 - config_name: ml-te features: - name: translation dtype: translation: languages: - ml - te splits: - name: train num_bytes: 9690153 num_examples: 10480 download_size: 393352875 dataset_size: 9690153 - config_name: pa-ur features: - name: translation dtype: translation: languages: - pa - ur splits: - name: train num_bytes: 34959176 num_examples: 51831 download_size: 393352875 dataset_size: 34959176 - config_name: hi-ur features: - name: translation dtype: translation: languages: - hi - ur splits: - name: train num_bytes: 81262590 num_examples: 109951 download_size: 393352875 dataset_size: 81262590 - config_name: mr-or features: - name: translation dtype: translation: languages: - mr - or splits: - name: train num_bytes: 33998805 num_examples: 47001 download_size: 393352875 dataset_size: 33998805 - config_name: en-ur features: - name: translation dtype: translation: languages: - en - ur splits: - name: train num_bytes: 100571795 num_examples: 202578 download_size: 393352875 dataset_size: 100571795 - config_name: ml-ur features: - name: translation dtype: translation: languages: - ml - ur splits: - name: train num_bytes: 15663718 num_examples: 20913 download_size: 393352875 dataset_size: 15663718 - config_name: bn-mr features: - name: translation dtype: translation: languages: - bn - mr splits: - name: train num_bytes: 27604502 num_examples: 34043 download_size: 393352875 dataset_size: 27604502 - config_name: gu-ta features: - name: translation dtype: translation: languages: - gu - ta splits: - name: train num_bytes: 25089131 num_examples: 29187 download_size: 393352875 dataset_size: 25089131 - config_name: pa-te features: - name: translation dtype: translation: languages: - pa - te splits: - name: train num_bytes: 23119690 num_examples: 25684 download_size: 393352875 dataset_size: 23119690 - config_name: bn-gu features: - name: translation dtype: translation: languages: - bn - gu splits: - name: train num_bytes: 19899277 num_examples: 25166 download_size: 393352875 dataset_size: 19899277 - config_name: bn-ur features: - name: translation dtype: translation: languages: - bn - ur splits: - name: train num_bytes: 27540215 num_examples: 39290 download_size: 393352875 dataset_size: 27540215 - config_name: ml-mr features: - name: translation dtype: translation: languages: - ml - mr splits: - name: train num_bytes: 19723458 num_examples: 22796 download_size: 393352875 dataset_size: 19723458 - config_name: or-ta features: - name: translation dtype: translation: languages: - or - ta splits: - name: train num_bytes: 35357904 num_examples: 44035 download_size: 393352875 dataset_size: 35357904 - config_name: ta-te features: - name: translation dtype: translation: languages: - ta - te splits: - name: train num_bytes: 17415768 num_examples: 17359 download_size: 393352875 dataset_size: 17415768 - config_name: gu-or features: - name: translation dtype: translation: languages: - gu - or splits: - name: train num_bytes: 20111876 num_examples: 27162 download_size: 393352875 dataset_size: 20111876 - config_name: en-gu features: - name: translation dtype: translation: languages: - en - gu splits: - name: train num_bytes: 33630906 num_examples: 59739 download_size: 393352875 dataset_size: 33630906 - config_name: hi-mr features: - name: translation dtype: translation: languages: - hi - mr splits: - name: train num_bytes: 55680473 num_examples: 69186 download_size: 393352875 dataset_size: 55680473 - config_name: mr-ta features: - name: translation dtype: translation: languages: - mr - ta splits: - name: train num_bytes: 41585343 num_examples: 48535 download_size: 393352875 dataset_size: 41585343 - config_name: en-mr features: - name: translation dtype: translation: languages: - en - mr splits: - name: train num_bytes: 65042597 num_examples: 117199 download_size: 393352875 dataset_size: 65042597 config_names: - bn-en - bn-gu - bn-hi - bn-ml - bn-mr - bn-or - bn-pa - bn-ta - bn-te - bn-ur - en-gu - en-hi - en-ml - en-mr - en-or - en-pa - en-ta - en-te - en-ur - gu-hi - gu-ml - gu-mr - gu-or - gu-pa - gu-ta - gu-te - gu-ur - hi-ml - hi-mr - hi-or - hi-pa - hi-ta - hi-te - hi-ur - ml-mr - ml-or - ml-pa - ml-ta - ml-te - ml-ur - mr-or - mr-pa - mr-ta - mr-te - mr-ur - or-pa - or-ta - or-te - or-ur - pa-ta - pa-te - pa-ur - ta-te - ta-ur - te-ur --- # Dataset Card for CVIT PIB ## 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) - [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: http://preon.iiit.ac.in/~jerin/bhasha/ - Paper: https://arxiv.org/abs/2008.04860 - Point of Contact: [Mailing List](cvit-bhasha@googlegroups.com) ### Dataset Summary This dataset is the large scale sentence aligned corpus in 11 Indian languages, viz. CVIT-PIB corpus that is the largest multilingual corpus available for Indian languages. ### Supported Tasks and Leaderboards - Machine Translation ### Languages Parallel data for following languages [en, bn, gu, hi, ml, mr, pa, or, ta, te, ur] are covered. ## Dataset Structure ### Data Instances An example for the "gu-pa" language pair: ``` { 'translation': { 'gu': 'એવો નિર્ણય લેવાયો હતો કે ખંતપૂર્વકની કામગીરી હાથ ધરવા, કાયદેસર અને ટેકનિકલ મૂલ્યાંકન કરવા, વેન્ચર કેપિટલ ઇન્વેસ્ટમેન્ટ સમિતિની બેઠક યોજવા વગેરે એઆઇએફને કરવામાં આવેલ પ્રતિબદ્ધતાના 0.50 ટકા સુધી અને બાકીની રકમ એફએફએસને પૂર્ણ કરવામાં આવશે.', 'pa': 'ਇਹ ਵੀ ਫੈਸਲਾ ਕੀਤਾ ਗਿਆ ਕਿ ਐੱਫਆਈਆਈ ਅਤੇ ਬਕਾਏ ਲਈ ਕੀਤੀਆਂ ਗਈਆਂ ਵਚਨਬੱਧਤਾਵਾਂ ਦੇ 0.50 % ਦੀ ਸੀਮਾ ਤੱਕ ਐੱਫਈਐੱਸ ਨੂੰ ਮਿਲਿਆ ਜਾਏਗਾ, ਇਸ ਨਾਲ ਉੱਦਮ ਪੂੰਜੀ ਨਿਵੇਸ਼ ਕਮੇਟੀ ਦੀ ਬੈਠਕ ਦਾ ਆਯੋਜਨ ਉਚਿਤ ਸਾਵਧਾਨੀ, ਕਾਨੂੰਨੀ ਅਤੇ ਤਕਨੀਕੀ ਮੁੱਲਾਂਕਣ ਲਈ ਸੰਚਾਲਨ ਖਰਚ ਆਦਿ ਦੀ ਪੂਰਤੀ ਹੋਵੇਗੀ।' } } ``` ### Data Fields - `translation`: Translation field containing the parallel text for the pair of languages. ### Data Splits The dataset is in a single "train" split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license. ### Citation Information ``` @inproceedings{siripragada-etal-2020-multilingual, title = "A Multilingual Parallel Corpora Collection Effort for {I}ndian Languages", author = "Siripragada, Shashank and Philip, Jerin and Namboodiri, Vinay P. and Jawahar, C V", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.462", pages = "3743--3751", language = "English", ISBN = "979-10-95546-34-4", } @article{2020, title={Revisiting Low Resource Status of Indian Languages in Machine Translation}, url={http://dx.doi.org/10.1145/3430984.3431026}, DOI={10.1145/3430984.3431026}, journal={8th ACM IKDD CODS and 26th COMAD}, publisher={ACM}, author={Philip, Jerin and Siripragada, Shashank and Namboodiri, Vinay P. and Jawahar, C. V.}, year={2020}, month={Dec} } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset, and [@albertvillanova](https://github.com/albertvillanova) for updating its version.
joey234/mmlu-jurisprudence-dev	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string splits: - name: dev num_bytes: 2700 num_examples: 5 download_size: 6711 dataset_size: 2700 --- # Dataset Card for "mmlu-jurisprudence-dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/c06e4969	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 200 num_examples: 10 download_size: 1394 dataset_size: 200 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c06e4969" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
izou3/MaskFormer_DB	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 125142150.192 num_examples: 7968 - name: test num_bytes: 21986379.57 num_examples: 1405 download_size: 21304292 dataset_size: 147128529.762 --- # Dataset Card for "MaskFormer_DB" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vikp/evol_codealpaca_filtered_87k	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: quality_prob dtype: float64 - name: learning_prob dtype: float64 splits: - name: train num_bytes: 194291512.64351812 num_examples: 87705 download_size: 107933444 dataset_size: 194291512.64351812 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "evol_codealpaca_filtered_86k" Filtered version of `theblackcat102/evol-codealpaca-v1`, with manual filtering, and automatic filtering based on quality and learning value classifiers.
bond005/sova_rudevices	--- pretty_name: RuDevices annotations_creators: - expert-generated language_creators: - crowdsourced language: - ru license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: size_categories: - 10K<n<100k source_datasets: - extended task_categories: - automatic-speech-recognition - audio-classification --- # Dataset Card for sova_rudevices ## 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: [SOVA RuDevices](https://github.com/sovaai/sova-dataset) - Repository: [SOVA Dataset](https://github.com/sovaai/sova-dataset) - Leaderboard: [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - Point of Contact: [SOVA.ai](mailto:support@sova.ai) ### Dataset Summary SOVA Dataset is free public STT/ASR dataset. It consists of several parts, one of them is SOVA RuDevices. This part is an acoustic corpus of approximately 100 hours of 16kHz Russian live speech with manual annotating, prepared by [SOVA.ai team](https://github.com/sovaai). Authors do not divide the dataset into train, validation and test subsets. Therefore, I was compelled to prepare this splitting. The training subset includes more than 82 hours, the validation subset includes approximately 6 hours, and the test subset includes approximately 6 hours too. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. ### Languages The audio is in Russian. ## Dataset Structure ### Data Instances A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided. ``` {'audio': {'path': '/home/bond005/datasets/sova_rudevices/data/train/00003ec0-1257-42d1-b475-db1cd548092e.wav', 'array': array([ 0.00787354, 0.00735474, 0.00714111, ..., -0.00018311, -0.00015259, -0.00018311]), dtype=float32), 'sampling_rate': 16000}, 'transcription': 'мне получше стало'} ``` ### Data Fields - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - transcription: the transcription of the audio file. ### Data Splits This dataset consists of three splits: training, validation, and test. This splitting was realized with accounting of internal structure of SOVA RuDevices (the validation split is based on the subdirectory `0`, and the test split is based on the subdirectory `1` of the original dataset), but audio recordings of the same speakers can be in different splits at the same time (the opposite is not guaranteed). \| \| Train \| Validation \| Test \| \| ----- \| ------ \| ---------- \| ----- \| \| examples \| 81607 \| 5835 \| 5799 \| \| hours \| 82.4h \| 5.9h \| 5.8h \| ## 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 All recorded audio files were manually annotated. #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Egor Zubarev, Timofey Moskalets, and SOVA.ai team. ### Licensing Information [Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @misc{sova2021rudevices, author = {Zubarev, Egor and Moskalets, Timofey and SOVA.ai}, title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech}, publisher = {GitHub}, journal = {GitHub repository}, year = {2021}, howpublished = {\url{https://github.com/sovaai/sova-dataset}}, } ``` ### Contributions Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
AleAle2423/Table_of_contents	--- license: mit language: - en tags: - synthetic pretty_name: Table of contents data set size_categories: - 10K<n<100K ---
KaiLv/UDR_CosmosQA	--- dataset_info: features: - name: idx dtype: int64 - name: question dtype: string - name: label dtype: string - name: choices dtype: string - name: len_question dtype: int64 - name: max_len_choices dtype: int64 splits: - name: train num_bytes: 11188271 num_examples: 18770 - name: test num_bytes: 3979297 num_examples: 6030 - name: validation num_bytes: 1722925 num_examples: 2603 - name: debug num_bytes: 2985534 num_examples: 5000 download_size: 11095169 dataset_size: 19876027 --- # Dataset Card for "UDR_CosmosQA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nlpyeditepe/tr-qnli	--- annotations_creators: - found language_creators: - machine-generated language: - tr-TR license: - mit multilinguality: - monolingual pretty_name: QNLI for Turkish size_categories: - unknown source_datasets: - extended\|glue task_categories: - text-classification task_ids: - natural-language-inference ---
anan-2024/twitter_dataset_1713009432	--- 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: 24140 num_examples: 56 download_size: 12841 dataset_size: 24140 configs: - config_name: default data_files: - split: train path: data/train-* ---
chmourya/openassistant-mourya	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1797495 num_examples: 1500 download_size: 1030938 dataset_size: 1797495 configs: - config_name: default data_files: - split: train path: data/train-* ---
salgadev/spirit-patterns	--- license: lgpl task_categories: - image-classification tags: - chemistry - spirits - beverages size_categories: - n<1K language: - en pretty_name: SpiritPatterns ---
dubeyam/PairRM-Preference-LIMA-Dataset	--- license: mit dataset_info: features: - name: data struct: - name: '##Question' dtype: string - name: '##Best Answer' dtype: string - name: '##Worst Answer' dtype: string splits: - name: train num_bytes: 44058 num_examples: 50 download_size: 33801 dataset_size: 44058 configs: - config_name: default data_files: - split: train path: data/train-* ---
terru3/tokenized_tinystories384	--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 5998238715 num_examples: 2119719 - name: validation num_bytes: 61703030 num_examples: 21990 download_size: 1771825341 dataset_size: 6059941745 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
ymoslem/Law-StackExchange	--- license: cc-by-sa-4.0 task_categories: - question-answering - text-classification - sentence-similarity language: - en tags: - legal pretty_name: Law Stack Exchange Questions and Answers size_categories: - 10K<n<100K --- All StackExchange legal questions and their answers from the Law site, up to 14 August 2023. The repository includes a notebook for the process using the official StackExchange API.
Brizape/Variome_0404	--- dataset_info: features: - name: id dtype: string - name: texts dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 splits: - name: train num_bytes: 8210534 num_examples: 96 - name: test num_bytes: 1447445 num_examples: 24 download_size: 972009 dataset_size: 9657979 --- # Dataset Card for "Variome_0404" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ekim15/bone_marrow_cell_dataset	--- license: cc-by-4.0 dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ABE '1': ART '2': BAS '3': BLA '4': EBO '5': EOS '6': FGC '7': HAC '8': KSC '9': LYI '10': LYT '11': MMZ '12': MON '13': MYB '14': NGB '15': NGS '16': NIF '17': OTH '18': PEB '19': PLM '20': PMO splits: - name: train num_bytes: 5531894343.482 num_examples: 137093 - name: validation num_bytes: 688690986.192 num_examples: 17146 - name: test num_bytes: 691641698.035 num_examples: 17135 download_size: 6935845206 dataset_size: 6912227027.709001 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* task_categories: - image-classification - unconditional-image-generation tags: - biology - medical size_categories: - 100K<n<1M --- ## About This Dataset Bone marrow biopsy is procedure applied to collect and examine bone marrow — the spongy tissue inside some of your larger bones. This biopsy can show whether your bone marrow is healthy and making normal amounts of blood cells. Doctors use these procedures to diagnose and monitor blood and marrow diseases, cancers, as well as fevers of unknown origin. The dataset contains a collection of over 170,000 de-identified, expert-annotated cells from the bone marrow smears of 945 patients stained using the May-Grünwald-Giemsa/Pappenheim stain. The diagnosis distribution in the cohort included a variety of hematological diseases reflective of the sample entry of a large laboratory specialized in leukemia diagnostics. Image acquisition was performed using a brightfield microscope with 40x magnification and oil immersion. All samples were processed in the Munich Leukemia Laboratory (MLL), scanned using equipment developed at Fraunhofer IIS and post-processed using software developed at Helmholtz Munich. ## How to Use this dataset - Create a multi-classification model to predict cell abnormalities; - Create a binary-classification model to predict if a cell is normal or not. - Create image generation model to add to training datset ## Acknowledgements Citation Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., & Marr, C. (2021). An Expert-Annotated Dataset of Bone Marrow Cytology in Hematologic Malignancies [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.AXH3-T579 Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., and Marr, C. (2021). Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image dataset. https://doi.org/10.1182/blood.2020010568 License CC BY 4.0 https://www.kaggle.com/datasets/andrewmvd/bone-marrow-cell-classification/data
neohack22/temporary-dataset	--- license: apache-2.0 ---
GabrielVidal/dalle-3-palette	--- dataset_info: features: - name: image dtype: image - name: palette dtype: image - name: text dtype: string splits: - name: train num_bytes: 1778362423 num_examples: 1000 download_size: 1778260113 dataset_size: 1778362423 configs: - config_name: default data_files: - split: train path: data/train-* license: cc0-1.0 language: - en size_categories: - n<1K tags: - image-text-dataset - synthetic-dataset ---
huggingnft/boredapeyachtclub	--- tags: - huggingnft - nft - huggan - gan - image - images task: - unconditional-image-generation datasets: - huggingnft/boredapeyachtclub license: mit --- # Dataset Card ## Disclaimer All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder. ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft) - Repository: [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft) - 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) ### Dataset Summary NFT images dataset for unconditional generation. NFT collection available [here](https://opensea.io/collection/boredapeyachtclub). Model is available [here](https://huggingface.co/huggingnft/boredapeyachtclub). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingnft/boredapeyachtclub") ``` ## Dataset Structure [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Fields The data fields are the same among all splits. - `image`: an `image` feature. - `id`: an `int` feature. - `token_metadata`: a `str` feature. - `image_original_url`: a `str` feature. ### Data Splits [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 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{huggingnft, author={Aleksey Korshuk} year=2022 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft)
renumics/f1_demo_dataset	--- dataset_info: features: - name: Time dtype: duration[ns] - name: Driver dtype: string - name: DriverNumber dtype: string - name: LapTime dtype: float64 - name: LapNumber dtype: float64 - name: Stint dtype: float64 - name: PitOutTime dtype: duration[ns] - name: PitInTime dtype: duration[ns] - name: Sector1Time dtype: float64 - name: Sector2Time dtype: float64 - name: Sector3Time dtype: float64 - name: Sector1SessionTime dtype: duration[ns] - name: Sector2SessionTime dtype: duration[ns] - name: Sector3SessionTime dtype: duration[ns] - name: SpeedI1 dtype: float64 - name: SpeedI2 dtype: float64 - name: SpeedFL dtype: float64 - name: SpeedST dtype: float64 - name: IsPersonalBest dtype: bool - name: Compound dtype: string - name: TyreLife dtype: float64 - name: FreshTyre dtype: bool - name: Team dtype: string - name: LapStartTime dtype: duration[ns] - name: LapStartDate dtype: timestamp[ns] - name: TrackStatus dtype: string - name: Position dtype: float64 - name: Deleted dtype: bool - name: DeletedReason dtype: string - name: FastF1Generated dtype: bool - name: IsAccurate dtype: bool - name: speed sequence: sequence: float64 - name: throttle sequence: sequence: float64 - name: drs sequence: sequence: float64 - name: nGear sequence: sequence: float64 - name: brake sequence: sequence: float64 - name: x sequence: sequence: float64 - name: y sequence: sequence: float64 - name: z sequence: sequence: float64 - name: distance_driver sequence: sequence: float64 - name: speed_emb sequence: float64 - name: brake_emb sequence: float64 - name: throttle_emb sequence: float64 - name: x_emb dtype: float64 - name: y_emb dtype: float64 - name: z_emb dtype: float64 - name: gear_vis dtype: string - name: speed_vis dtype: string - name: portrait dtype: string - name: brake_emb_reduced sequence: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 22426400 num_examples: 201 download_size: 15371945 dataset_size: 22426400 --- # Dataset Card for "f1_demo_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rcds/lower_court_insertion_swiss_judgment_prediction	--- annotations_creators: - expert-generated language: - de - fr - it - en language_creators: - expert-generated - found license: - cc-by-sa-4.0 multilinguality: - multilingual pretty_name: LowerCourtInsertionSwissJudgmentPrediction size_categories: - 1K<n<10K source_datasets: - extended\|swiss_judgment_prediction tags: - explainability-judgment-prediction task_categories: - text-classification - other task_ids: [] --- # Dataset Card for "LowerCourtInsertionSwissJudgmentPrediction": An implementation of lower court insertion bias analysis for Swiss judgment prediction ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Summary](#dataset-summary) - [Documents](#documents) - [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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Summary This dataset contains an implementation of lower-court-insertion for the SwissJudgmentPrediction task. Note that this dataset only provides a test set and should be used in comination with the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset. ### Documents Lower-Court-Insertion-Swiss-Judgment-Prediction is a subset of the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset. The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Lower-Court-Insertion-Swiss-Judgment-Prediction extends this dataset by adding lower court insertion. ### Supported Tasks and Leaderboards LowerCourtInsertionSwissJudgmentPrediction can be used for performing the LowerCourtInsertion in the legal judgment prediction task. ### Languages Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings. ## Dataset structure ### Data Instances #### Multilingual use of the dataset When the dataset is used in a multilingual setting selecting the the 'all' flag: ```python from datasets import load_dataset dataset = load_dataset('rcds/lower_court_insertion_swiss_judgment_prediction', 'all') ``` #### Monolingual use of the dataset When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example: ```python from datasets import load_dataset dataset = load_dataset('rcds/lower-court-insertion_swiss_judgment_prediction', 'de') ``` ### Data Fields The following data fields are provided for documents (test): id: (int) a unique identifier of the for the document<br/> year: (int) the publication year<br/> label: (str) the judgment outcome: dismissal or approval<br/> language: (str) one of (de, fr, it)<br/> region: (str) the region of the lower court<br/> canton: (str) the canton of the lower court<br/> legal area: (str) the legal area of the case<br/> explainability_label: (str) the explainability label assigned to the occluded text: (Lower court, Baseline)<br/> text: (str) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)<br/> lower_court: (str) the inserted lower_court (for Baseline there is no insertion)<br/> ### Data Splits (Including Swiss Judgment Prediction) Language \| Subset \| Number of Rows (Test) \|-----\|-----\|------\| German\| de\| __378__ French \| fr\| __414__ Italian \| it\| __335__ All \| all \| __1127__ Language \| Subset \| Number of Documents (Test) \| ----------- \| ----------- \| ----------- \| German\| de \| __38__ French \| fr \| __36__ Italian \| it \| __34__ All \| all \| __108__ ## Dataset Creation ### Curation Rationale The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner. ### Source Data #### Initial Data Collection and Normalization The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. #### Who are the source language producers? Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition the a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts with the lower court. These lower court annotations were then use the insert each lower court into each case once (instead of the original lower court). Allowing an analysis of the changes in the models performance for each inserted lower court, giving insight into a possible bias among them. The legal expert annotation were conducted from April 2020 to August 2020. #### Who are the annotators? Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level). ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html. ## Additional Information ### Dataset Curators Niklaus et al. (2021) and Nina Baumgartner ### Licensing Information We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf) © Swiss Federal Supreme Court, 2000-2020 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf ### Citation Information ``` @misc{baumgartner_nina_occlusion_2019, title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland}, shorttitle = {From Occlusion to Transparancy}, abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.}, author = {{Baumgartner, Nina}}, year = {2022}, langid = {english} } ``` ### Contributions Thanks to [@ninabaumgartner](https://github.com/ninabaumgartner) for adding this dataset.
OpenGVLab/Caption-Evaluation-Data	--- license: apache-2.0 --- This repository presents the evaluation data used for [ASM](https://github.com/Weiyun1025/all-seeing-official/tree/main/all-seeing). Please refer to [this document](https://github.com/OpenGVLab/all-seeing/tree/main/all-seeing#testing) for more details about the repository.
HumanCompatibleAI/random-seals-Walker2d-v1	--- dataset_info: features: - name: obs sequence: sequence: float64 - name: acts sequence: sequence: float32 - name: infos sequence: string - name: terminal dtype: bool - name: rews sequence: float32 splits: - name: train num_bytes: 81303050 num_examples: 100 download_size: 41495120 dataset_size: 81303050 --- # Dataset Card for "random-seals-Walker2d-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zolak/twitter_dataset_50_1713123422	--- 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: 244794 num_examples: 572 download_size: 121577 dataset_size: 244794 configs: - config_name: default data_files: - split: train path: data/train-* ---
Oztobuzz/gsm8k_0.1_42	--- license: mit ---
rkorez/medix-codegen-v2	--- dataset_info: features: - name: index dtype: int64 - name: repo_id dtype: string - name: file_path dtype: string - name: content dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 206046946 num_examples: 30000 download_size: 61887714 dataset_size: 206046946 configs: - config_name: default data_files: - split: train path: data/train-* ---
QNN/autotrain-data-pubmed	--- task_categories: - token-classification --- # AutoTrain Dataset for project: pubmed ## Dataset Description This dataset has been automatically processed by AutoTrain for project pubmed. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "tokens": [ "Pd", "has", "been", "regarded", "as", "one", "of", "the", "alternatives", "to", "Pt", "as", "a", "promising", "hydrogen", "evolution", "reaction", "(HER)", "catalyst.", "Strategies", "including", "Pd-metal", "alloys", "(Pd-M)", "and", "Pd", "hydrides", "(PdH<sub><i>x</i></sub>)", "have", "been", "proposed", "to", "boost", "HER", "performances.", "However,", "the", "stability", "issues,", "e.g.,", "the", "dissolution", "in", "Pd-M", "and", "the", "hydrogen", "releasing", "in", "PdH<sub><i>x</i></sub>,", "restrict", "the", "industrial", "application", "of", "Pd-based", "HER", "catalysts.", "We", "here", "design", "and", "synthesize", "a", "stable", "Pd-Cu", "hydride", "(", "PdCu<sub>0.2</sub>H<sub>0.43</sub>", ")", "catalyst,", "combining", "the", "advantages", "of", "both", "Pd-M", "and", "PdH<sub><i>x</i></sub>", "structures", "and", "improving", "the", "HER", "durability", "simultaneously.", "The", "hydrogen", "intercalation", "is", "realized", "under", "atmospheric", "pressure", "(1.0", "atm)", "following", "our", "synthetic", "approach", "that", "imparts", "high", "stability", "to", "the", "Pd-Cu", "hydride", "structure.", "The", "obtained", "PdCu<sub>0.2</sub>H<sub>0.43</sub>", "catalyst", "exhibits", "a", "small", "overpotential", "of", "28", "mV", "at", "10", "mA/cm<sup>2</sup>", ",", "a", "low", "Tafel", "slope", "of", "23", "mV/dec", ",", "and", "excellent", "HER", "durability", "due", "to", "its", "appropriate", "hydrogen", "adsorption", "free", "energy", "and", "alleviated", "metal", "dissolution", "rate.", "</p>", "<p>" ], "tags": [ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 4, 2, 5, 5, 2, 5, 5, 2, 2, 2, 4, 2, 2, 5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ] }, { "tokens": [ "A", "critical", "challenge", "in", "energy", "research", "is", "the", "development", "of", "earth", "abundant", "and", "cost-effective", "materials", "that", "catalyze", "the", "electrochemical", "splitting", "of", "water", "into", "hydrogen", "and", "oxygen", "at", "high", "rates", "and", "low", "overpotentials.", "Key", "to", "addressing", "this", "issue", "lies", "not", "only", "in", "the", "synthesis", "of", "new", "materials,", "but", "also", "in", "the", "elucidation", "of", "their", "active", "sites,", "their", "structure", "under", "operating", "conditions", "and", "ultimately,", "extraction", "of", "the", "structure-function", "relationships", "used", "to", "spearhead", "the", "next", "generation", "of", "catalyst", "development.", "In", "this", "work,", "we", "present", "a", "complete", "cycle", "of", "synthesis,", "operando", "characterization,", "and", "redesign", "of", "an", "amorphous", "cobalt", "phosphide", "(", "CoP", "<sub><i>x</i></sub>", ")", "bifunctional", "catalyst.", "The", "research", "was", "driven", "by", "integrated", "electrochemical", "analysis,", "Raman", "spectroscopy", "and", "gravimetric", "measurements", "utilizing", "a", "novel", "quartz", "crystal", "microbalance", "spectroelectrochemical", "cell", "to", "uncover", "the", "catalytically", "active", "species", "of", "amorphous", "CoP", "<sub><i>x</i></sub>", "and", "subsequently", "modify", "the", "material", "to", "enhance", "the", "activity", "of", "the", "elucidated", "catalytic", "phases.", "Illustrating", "the", "power", "of", "our", "approach,", "the", "second", "generation", "cobalt-iron", "phosphide", "(", "CoFeP<sub>x</sub>", ")", "catalyst,", "developed", "through", "an", "iteration", "of", "the", "operando", "measurement", "directed", "optimization", "cycle,", "is", "superior", "in", "both", "hydrogen", "and", "oxygen", "evolution", "reactivity", "over", "the", "previous", "material", "and", "is", "capable", "of", "overall", "water", "electrolysis", "at", "a", "current", "density", "of", "10", "mA", "cm<sup>-2</sup>", "with", "1.5", "V", "applied", "bias", "in", "1", "M", "KOH", "electrolyte", "solution.", "</p>", "<p>" ], "tags": [ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2, 5, 5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ] } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)", "tags": "Sequence(feature=ClassLabel(names=['CATALYST', 'CO-CATALYST', 'O', 'Other', 'PROPERTY_NAME', 'PROPERTY_VALUE'], id=None), length=-1, id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 166 \| \| valid \| 44 \|
ssahir/REPV	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: path dtype: string - name: file dtype: string - name: gender dtype: string - name: emotion dtype: string - name: speech sequence: float32 splits: - name: train num_bytes: 380197186 num_examples: 1628 - name: test num_bytes: 92682047 num_examples: 407 download_size: 0 dataset_size: 472879233 --- # Dataset Card for "REPV" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
felixsaaro/test	--- dataset_info: features: - name: image dtype: image - name: name dtype: string - name: frameType dtype: string splits: - name: train num_bytes: 660338115.545 num_examples: 12405 download_size: 656146541 dataset_size: 660338115.545 license: apache-2.0 task_categories: - text-to-image language: - en pretty_name: Yugio Cards size_categories: - 1K<n<10K --- # Dataset Card for "test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ISTNetworks/open-orca-arabic	--- license: other license_name: ist license_link: LICENSE ---
AlekseyKorshuk/full_user_edit_responses-clean	--- dataset_info: features: - name: message_id dtype: string - name: model_input dtype: string - name: response dtype: string - name: edited_response dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 782450796.338342 num_examples: 364272 download_size: 355765300 dataset_size: 782450796.338342 --- # Dataset Card for "full_user_edit_responses-clean" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zolak/twitter_dataset_81_1713170079	--- 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: 431921 num_examples: 996 download_size: 201715 dataset_size: 431921 configs: - config_name: default data_files: - split: train path: data/train-* ---
ranimeree/NewDataSetMixed	--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 395458124.688 num_examples: 2769 - name: validation num_bytes: 61731350.0 num_examples: 352 - name: test num_bytes: 12103649.0 num_examples: 101 download_size: 452227024 dataset_size: 469293123.688 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
genvision-ai/test_2024_02_20	--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: segments_info list: - name: area dtype: float64 - name: bbox sequence: float64 - name: category_id dtype: int64 - name: id dtype: int64 - name: image_id dtype: int64 - name: iscrowd dtype: int64 - name: segmentation sequence: sequence: float64 - name: image_name dtype: string splits: - name: train num_bytes: 2721373626.738 num_examples: 12039 download_size: 2737790368 dataset_size: 2721373626.738 configs: - config_name: default data_files: - split: train path: data/train-* ---
lolechka/guanaco-llama2-ru-1k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 166211.8171846435 num_examples: 788 - name: test num_bytes: 9932.181467181466 num_examples: 46 download_size: 1056715 dataset_size: 176143.99865182498 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
nimrita/BhagwadGitaChapter2Embeddings	--- license: afl-3.0 --- This file contains embeddings of English text of Bhagwad Gita Chapter 2 from this site (https://vedabase.io/en/library/bg/2/) generated using sentence-transformer (sentence-transformers/all-MiniLM-L6-v2) at Hugging Face.
retkowski/ytseg	--- license: cc-by-nc-sa-4.0 language: - en tags: - text segmentation - smart chaptering - segmentation - youtube - asr pretty_name: YTSeg size_categories: - 10K<n<100K task_categories: - token-classification - automatic-speech-recognition --- # From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions We present <span style="font-variant:small-caps; font-weight:700;">YTSeg</span>, a topically and structurally diverse benchmark for the text segmentation task based on YouTube transcriptions. The dataset comprises 19,299 videos from 393 channels, amounting to 6,533 content hours. The topics are wide-ranging, covering domains such as science, lifestyle, politics, health, economy, and technology. The videos are from various types of content formats, such as podcasts, lectures, news, corporate events \& promotional content, and, more broadly, videos from individual content creators. We refer to the paper ([acl](https://aclanthology.org/2024.eacl-long.25/) \| [arXiv](https://arxiv.org/abs/2402.17633)) for further information. We provide both text and audio data as well as a download script for the video data. ## Data Overview ### <span style="font-variant:small-caps;">YTSeg</span> Each video is represented as a JSON object with the following fields: \| Field \| Description \| \|--------------\|------------------------------------------------\| \| `text` \| A flat list of sentences. \| \| `targets` \| The target segmentation as string of binary values (e.g., `000100000010`). \| \| `channel_id` \| The YouTube channel ID which this video belongs to. \| \| `video_id` \| The YouTube video ID. \| \| `audio_path` \| Path to the .mp3 file of the video. \| \| Partition \| # Examples \| \|------------\|--------------\| \| Training \| 16,404 (85%) \| \| Validation \| 1,447 (7.5%) \| \| Testing \| 1,448 (7.5%) \| \| Total \| 19,229 \| ### <span style="font-variant:small-caps;">YTSeg[Titles]</span> Each chapter of a video is represented as a JSON object with the following fields: \| Field \| Description \| \|--------------\|------------------------------------------------\| \| `input` \| The complete chapter/section text. \| \| `input_with_chapters` \| The complete chapter/section text with previous section titles prepended. \| \| `target` \| The target chapter title. \| \| `channel_id` \| The YouTube channel ID which this chapter's video belongs to. \| \| `video_id` \| The YouTube video ID which this chapter belongs to. \| \| `chapter_idx` \| The index and placement of the chapter in the video (e.g., the first chapter has index `0`). \| \| Partition \| # Examples \| \|------------\|--------------\| \| Training \| 146,907 (84.8%)\| \| Validation \| 13,206 (7.6%) \| \| Testing \| 13,082 (7.6%) \| \| Total \| 173,195 \| ### Audio Data We provide audio files for all examples in the dataset, preprocessed into the .mp3 format with a standardized sample rate of 16,000 Hz and a single channel (mono). These files are organized within the directory structure as follows: `data/audio/<channel_id>/<video_id>.mp3`. ### Video Data A download script for the video and audio data is provided. ```py python download_videos.py ``` In the script, you can further specify a target folder (default is `./video`) and target formats in a priority list. ## Loading Text Data This repository comes with a simple, exemplary script to read in the text data with `pandas`. ```py from load_data import get_partition test_data = get_partition('test') ``` Equivalently, to read in <span style="font-variant:small-caps;">YTSeg[Titles]</span>: ```py from load_data import get_title_partition test_data = get_title_partition('test') ``` ## Citing We kindly request you to cite our corresponding EACL 2024 paper if you use our dataset. ``` @inproceedings{retkowski-waibel-2024-text, title = "From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions", author = "Retkowski, Fabian and Waibel, Alexander", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-long.25", pages = "406--419", abstract = "Text segmentation is a fundamental task in natural language processing, where documents are split into contiguous sections. However, prior research in this area has been constrained by limited datasets, which are either small in scale, synthesized, or only contain well-structured documents. In this paper, we address these limitations by introducing a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. As part of this work, we introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines. Lastly, we expand the notion of text segmentation to a more practical {``}smart chaptering{''} task that involves the segmentation of unstructured content, the generation of meaningful segment titles, and a potential real-time application of the models.", } ``` ## License The dataset is available under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) 4.0 license. We note that we do not own the copyright of the videos and as such opted to release the dataset with a non-commercial license, with the intended use to be in research and education.
Multimodal-Fatima/VQAv2_test_no_image_split_3	--- dataset_info: features: - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: id_image dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: Attributes_ViT_L_14_descriptors_text_davinci_003_full sequence: string - name: clip_tags_ViT_L_14_wo_openai sequence: string - name: clip_tags_ViT_L_14_with_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_with_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_with_openai sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_module sequence: string - name: captions_module_filter sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string splits: - name: test num_bytes: 2150238682 num_examples: 44779 download_size: 540758200 dataset_size: 2150238682 --- # Dataset Card for "VQAv2_test_no_image_split_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686017	--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: natural_language_inference model: Jiva/xlm-roberta-large-it-mnli metrics: [] dataset_name: glue dataset_config: mnli dataset_split: validation_matched col_mapping: text1: premise text2: hypothesis target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Natural Language Inference * Model: Jiva/xlm-roberta-large-it-mnli * Dataset: glue * Config: mnli * Split: validation_matched To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
Brendan/BabyLMDemo	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2220547 num_examples: 80000 download_size: 0 dataset_size: 2220547 --- # Dataset Card for "BabyLMDemo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Cleudemir/basedevoz2	--- license: openrail ---
yazan-bawab/ps-llm	--- license: mit ---
tmnam20/ViPubMed	--- license: cc language: - vi - en task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: pubmed dataset_info: features: - name: en dtype: string - name: vi dtype: string splits: - name: pubmed22 num_bytes: 44360028980 num_examples: 20087006 download_size: 23041004247 dataset_size: 44360028980 --- # ALERT: This dataset repo is duplicated from [VietAI/vi_pubmed](https://huggingface.co/datasets/VietAI/vi_pubmed) The reason to have this duplicated repo is to avoid the lost/corruption of the original repo when I am doing some stuff ^^. # Dataset Summary 20M Vietnamese PubMed biomedical abstracts translated by the [state-of-the-art English-Vietnamese Translation project](https://arxiv.org/abs/2210.05610). The data has been used as unlabeled dataset for [pretraining a Vietnamese Biomedical-domain Transformer model](https://arxiv.org/abs/2210.05598). ![image](https://user-images.githubusercontent.com/44376091/200204462-4d559113-5bdf-4cc5-9e88-70abe82babba.png) image source: [Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation](https://arxiv.org/abs/2210.05598) # Language - English: Original biomedical abstracts from [Pubmed](https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html) - Vietnamese: Synthetic abstract translated by a [state-of-the-art English-Vietnamese Translation project](https://arxiv.org/abs/2210.05610) # Dataset Structure - The English sequences are - The Vietnamese sequences are # Source Data - Initial Data Collection and Normalization https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html # Licensing Information [Courtesy of the U.S. National Library of Medicine.](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html) # Citation ``` @misc{mtet, doi = {10.48550/ARXIV.2210.05610}, url = {https://arxiv.org/abs/2210.05610}, author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {MTet: Multi-domain Translation for English and Vietnamese}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` ``` @misc{vipubmed, doi = {10.48550/ARXIV.2210.05598}, url = {https://arxiv.org/abs/2210.05598}, author = {Phan, Long and Dang, Tai and Tran, Hieu and Phan, Vy and Chau, Lam D. and Trinh, Trieu H.}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
cbt	--- annotations_creators: - machine-generated language_creators: - found language: - en license: - gfdl multilinguality: - monolingual size_categories: - 100K<n<1M - n<1K source_datasets: - original task_categories: - other - question-answering task_ids: - multiple-choice-qa paperswithcode_id: cbt pretty_name: Children’s Book Test (CBT) config_names: - CN - NE - P - V - raw dataset_info: - config_name: CN features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 301730151 num_examples: 120769 - name: test num_bytes: 6138376 num_examples: 2500 - name: validation num_bytes: 4737257 num_examples: 2000 download_size: 31615166 dataset_size: 312605784 - config_name: NE features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 253551931 num_examples: 108719 - name: test num_bytes: 5707734 num_examples: 2500 - name: validation num_bytes: 4424316 num_examples: 2000 download_size: 29693075 dataset_size: 263683981 - config_name: P features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 852852601 num_examples: 334030 - name: test num_bytes: 6078048 num_examples: 2500 - name: validation num_bytes: 4776981 num_examples: 2000 download_size: 43825356 dataset_size: 863707630 - config_name: V features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 252177649 num_examples: 105825 - name: test num_bytes: 5806625 num_examples: 2500 - name: validation num_bytes: 4556425 num_examples: 2000 download_size: 29992082 dataset_size: 262540699 - config_name: raw features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 25741580 num_examples: 98 - name: test num_bytes: 1528704 num_examples: 5 - name: validation num_bytes: 1182657 num_examples: 5 download_size: 16350790 dataset_size: 28452941 configs: - config_name: CN data_files: - split: train path: CN/train-* - split: test path: CN/test-* - split: validation path: CN/validation-* - config_name: NE data_files: - split: train path: NE/train-* - split: test path: NE/test-* - split: validation path: NE/validation-* - config_name: P data_files: - split: train path: P/train-* - split: test path: P/test-* - split: validation path: P/validation-* - config_name: V data_files: - split: train path: V/train-* - split: test path: V/test-* - split: validation path: V/validation-* - config_name: raw data_files: - split: train path: raw/train-* - split: test path: raw/test-* - split: validation path: raw/validation-* --- # Dataset Card for CBT ## 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:[The bAbI project](https://research.fb.com/downloads/babi/) - Repository: - Paper: [arXiv Paper](https://arxiv.org/pdf/1511.02301.pdf) - Leaderboard: - Point of Contact: [Felix Hill](mailto:felix.hill@cl.cam.ac.uk) or [Antoine Bordes](mailto:abordes@fb.com). ### Dataset Summary The Children’s Book Test (CBT) is designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available. This dataset contains four different configurations: - `V`: where the answers to the questions are verbs. - `P`: where the answers to the questions are pronouns. - `NE`: where the answers to the questions are named entities. - `CN`: where the answers to the questions are common nouns. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The data is present in English language as written by authors Lucy Maud Montgomery, Charles Dickens,Andrew Lang, etc. in story books for children. ## Dataset Structure ### Data Instances An instance from the `V` config: ``` {'answer': 'said', 'options': ['christening', 'existed', 'hear', 'knows', 'read', 'remarked', 'said', 'sitting', 'talking', 'wearing'], 'question': "`` They are very kind old ladies in their way , '' XXXXX the king ; `` and were nice to me when I was a boy . ''", 'sentences': ['This vexed the king even more than the queen , who was very clever and learned , and who had hated dolls when she was a child .', 'However , she , too in spite of all the books she read and all the pictures she painted , would have been glad enough to be the mother of a little prince .', 'The king was anxious to consult the fairies , but the queen would not hear of such a thing .', 'She did not believe in fairies : she said that they had never existed ; and that she maintained , though The History of the Royal Family was full of chapters about nothing else .', 'Well , at long and at last they had a little boy , who was generally regarded as the finest baby that had ever been seen .', 'Even her majesty herself remarked that , though she could never believe all the courtiers told her , yet he certainly was a fine child -- a very fine child .', 'Now , the time drew near for the christening party , and the king and queen were sitting at breakfast in their summer parlour talking over it .', 'It was a splendid room , hung with portraits of the royal ancestors .', 'There was Cinderella , the grandmother of the reigning monarch , with her little foot in her glass slipper thrust out before her .', 'There was the Marquis de Carabas , who , as everyone knows , was raised to the throne as prince consort after his marriage with the daughter of the king of the period .', 'On the arm of the throne was seated his celebrated cat , wearing boots .', 'There , too , was a portrait of a beautiful lady , sound asleep : this was Madame La Belle au Bois-dormant , also an ancestress of the royal family .', 'Many other pictures of celebrated persons were hanging on the walls .', "`` You have asked all the right people , my dear ? ''", 'said the king .', "`` Everyone who should be asked , '' answered the queen .", "`` People are so touchy on these occasions , '' said his majesty .", "`` You have not forgotten any of our aunts ? ''", "`` No ; the old cats ! ''", "replied the queen ; for the king 's aunts were old-fashioned , and did not approve of her , and she knew it ."]} ``` ### Data Fields For the `raw` config, the data fields are: - `title`: a `string` feature containing the title of the book present in the dataset. - `content`: a `string` feature containing the content of the book present in the dataset. For all other configs, the data fields are: - `sentences`: a `list` of `string` features containing 20 sentences from a book. - `question`: a `string` feature containing a question with blank marked as `XXXX` which is to be filled with one of the options. - `answer`: a `string` feature containing the answer. - `options`: a `list` of `string` features containing the options for the question. ### Data Splits The splits and corresponding sizes are: \| \|train \|test \|validation\| \|:--\|------:\|----:\|---------:\| \|raw\|98 \|5 \|5 \| \|V \|105825 \|2500 \|2000 \| \|P \|334030 \|2500 \|2000 \| \|CN \|120769 \|2500 \|2000 \| \|NE \|108719 \|2500 \|2000 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Children's Book Authors ### Annotations #### Annotation process From the [homepage](https://research.fb.com/downloads/babi/): >After allocating books to either training, validation or test sets, we formed example ‘questions’ from chapters in the book by enumerating 21 consecutive sentences. In each question, the first 20 sentences form the context, and a word is removed from the 21st sentence, which becomes the query. Models must identify the answer word among a selection of 10 candidate answers appearing in the context sentences and the query. For finer-grained analyses, we evaluated four classes of question by removing distinct types of word: Named Entities, (Common) Nouns, Verbs and Prepositions. #### 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 ``` GNU Free Documentation License v1.3 ``` ### Citation Information ``` @misc{hill2016goldilocks, title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations}, author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston}, year={2016}, eprint={1511.02301}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.
marhensa/comfyui-workflow	--- language: - en tags: - comfyui - template - workflow --- # HOW TO USE: Drag and Drop Template.JSON / Template.PNG into [ComfyUI in browser](https://github.com/comfyanonymous/ComfyUI) to load the template! (Yes even output PNG file works as workflow template). # PREQUISITE INSTALLATION NOTES: It requires custom nodes and file requirements, it's easy: 1. ["ComfyUI Manager"](https://github.com/ltdrdata/ComfyUI-Manager#installation) (install it first). 2. Open ComfyUI WebUI > Manager > Install Custom Nodes, search "ComfyUI-Impact-Pack", "SDXL Prompt Sytler", and "Recommended SDXL Resolution Calculator", "Masquerade Nodes", Install ALL, close the ComfyUI console command prompt. 3. "Recommended SD 1.5 Resolution Calculator", open command prompt from this folder \ComfyUI\custom_nodes\ then use this command "git clone https://github.com/marhensa/sd15-recommended-res-calc.git") 4. "ComfyUI ControlNet Aux" custom node can be installed with [this instruction](https://github.com/Fannovel16/comfyui_controlnet_aux#installation), and the models can be downloaded [from here](https://huggingface.co/lllyasviel/sd_control_collection/tree/main), put into \ComfyUI\models\controlnet\ 5. LCM LoRA, (needs a LCM LoRA, download it from here for [LCM-SDXL](https://huggingface.co/latent-consistency/lcm-lora-sdxl/resolve/main/pytorch_lora_weights.safetensors?download=true), for [LCM-SDXL-SSD1B](https://huggingface.co/latent-consistency/lcm-lora-ssd-1b/resolve/main/pytorch_lora_weights.safetensors?download=true), and here for [LCM-SD1.5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5/resolve/main/pytorch_lora_weights.safetensors?download=true)), for LCM-SDXL put into \ComfyUI\models\loras\LCM\SDXL\, LCM-SDXL-SSD1B put into \ComfyUI\models\loras\LCM\SDXL-SSD1B\, LCM-SD1.5 put into \ComfyUI\models\loras\LCM\SD15\ . 6. LCM-Turbo, download from [here](https://civitai.com/api/download/models/246747?type=Model&format=SafeTensor) for SDXL based models, and then put into put into \ComfyUI\models\loras\SDXL\LCMTurboMix_Euler_A_fix.safetensors 7. Another LoRA, download additionals LoRA from CivitAI or select "None" to not use it. 8. VAE selector, (needs a VAE file, download SDXL BF16 VAE from [here](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl_vae.safetensors), and VAE file for SD 1.5 from [here](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors?download=true). Put into \ComfyUI\models\vae\SDXL\ and \ComfyUI\models\vae\SD15\). 9. Upscale model, (needs to be downloaded into \ComfyUI\models\upscale_models\ Recommended one is 4x-UltraSharp, download [from here](https://mega.nz/folder/qZRBmaIY#nIG8KyWFcGNTuMX_XNbJ_g). All the list of Upscale model is [here](https://upscale.wiki/w/index.php?title=Model_Database&oldid=1571)) 10. Checkpoints. SDXL-SSD1B can be downloaded from [here](https://huggingface.co/segmind/SSD-1B/resolve/main/SSD-1B.safetensors?download=true), my recommended Checkpoint for SDXL is [AlbedoBase-XL](https://civitai.com/models/140737), [Jib-Mix-Realistic-XL](https://civitai.com/models/194768), [Crystal-Clear-XL](https://civitai.com/models/122822), and for SD1.5 is [Haveall](https://civitai.com/models/213692), download Safetensors file and put into \ComfyUI\models\checkpoints\SDXL\ and \ComfyUI\models\checkpoints\SD15\ 11. Note: When loading a PNG Workflow from here, first click Refresh on ComfyUI menu, it will refresh models that you have on your PC, then choose it (Checkpoints, VAE, LoRA, Upscale Model, ControlNet models etc) accordingly. . > Custom Node "[Recommended Resolution Calculator](https://github.com/marhensa/sdxl-recommended-res-calc)", makes targeting final resolution much easier, because it will calculates recommended SD initial size and its upscale (or reverse upscale) value. You only need to input your FINAL target resolution with this custom node. . # WORKFLOW SELECTION: (drag and drop that PNG image file into ComfyUI interface, it will open up as workflow template) ## RECOMMENDED! FAST!! TIDY - Single SDXL Checkpoint Workflow (LCM-Turbo, PromptStyler, Upscale Model Switch, ControlNet, FaceDetailer) : ![SDXL-Tidy-LCMTurbo-PromptStyler-UpscaleModelSwitch-ControlNet-FaceDetailer.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/SDXL-Tidy-LCMTurbo-PromptStyler-UpscaleModelSwitch-ControlNet-FaceDetailer.png) (ControlNet image reference example: [halo.jpg](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/halo.jpg)) . ## TIDY - Single SD 1.5 Checkpoint Workflow (LCM, PromptStyler, Upscale Model Switch, ControlNet, FaceDetailer) : ![SD15-Tidy-LCM-PromptStyler-UpscaleModelSwitch-ControlNet-FaceDetailer.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/SD15-Tidy-LCM-PromptStyler-UpscaleModelSwitch-ControlNet-FaceDetailer.png) (ControlNet image reference example: [halo.jpg](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/halo.jpg)) . ## Breakdown - SDXL Workflow (LCM-Turbo, PromptStyler, Upscale Model Switch) : ![sdxl-lcmturbo-workflow.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/sdxl-lcmturbo-workflow.png) . ## Breakdown - SDXL-SSD1B Workflow (LCM, PromptStyler, Upscale Model Switch) : ![sdxl-lcm-workflow.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/sdxlssd1b-lcm-workflow.png) . ## Breakdown - SD1.5 Workflow (LCM, PromptStyler, Upscale Model Switch) : ![sdxl-lcm-workflow.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/sd15-lcm-workflow.png) . ## Simple without install anything - SDXL / SD15 LCM Workflow : ![SDXL_Single_Tidy_Simple-LCM-LoRA-VAE.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/SDXL_Single_Tidy_Simple-LCM-LoRA-VAE.png)
hustzx/sd_test	--- license: apache-2.0 ---
detakarang/know_sql_alpaca	--- license: openrail ---
youngdicey/rico-raw	--- license: openrail ---
gulyaabdullaeva/DS_test	--- license: unknown ---

uname-n/slim-orca-dedup-chat

--- dataset_info: features: - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 602329260 num_examples: 363491 download_size: 301939359 dataset_size: 602329260 configs: - config_name: default data_files: - split: train path: data/train-* ---

bhjhk/masmamad8

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

AdapterOcean/med_alpaca_standardized_cluster_86

--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 51349063 num_examples: 5266 download_size: 15035670 dataset_size: 51349063 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_86" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DynamicSuperb/SpeechDetection_LJSpeech

--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 58016282.72519084 num_examples: 200 download_size: 56990484 dataset_size: 58016282.72519084 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "speechDetection_LJSpeech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

myrtotsok/clf-3

--- dataset_info: features: - name: request dtype: string - name: label dtype: string splits: - name: train num_bytes: 121051 num_examples: 1120 - name: validation num_bytes: 30256 num_examples: 280 download_size: 28195 dataset_size: 151307 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

FINNUMBER/FINCH_TRAIN_ALL_900_per100_NEW_Rationale

--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: sub_task dtype: string - name: rationale dtype: string - name: correct dtype: bool - name: check dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 3764071 num_examples: 900 download_size: 2070353 dataset_size: 3764071 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/stg44_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of stg44/StG44/StG44 (Girls' Frontline) This is the dataset of stg44/StG44/StG44 (Girls' Frontline), containing 114 images and their tags. The core tags of this character are `blonde_hair, long_hair, green_eyes, hat, bangs, military_hat, black_headwear, 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 | 114 | 158.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 114 | 82.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 293 | 187.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 114 | 136.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 293 | 270.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stg44_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/stg44_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, black_necktie, military_uniform, solo, looking_at_viewer, white_gloves, white_shirt, black_jacket, smile, upper_body, simple_background, white_background, long_sleeves, closed_mouth, open_mouth | | 1 | 15 | ![](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_necktie, black_skirt, looking_at_viewer, military_uniform, solo, white_shirt, assault_rifle, white_gloves, black_jacket, closed_mouth, holding_gun, black_thighhighs, boots, collared_shirt, black_footwear, military_jacket, belt, long_sleeves, smile, full_body, peaked_cap, simple_background, standing | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, black_pantyhose, black_skirt, eyewear_on_head, high_heels, solo, sunglasses, white_shirt, black_footwear, full_body, looking_at_viewer, medium_breasts, pencil_skirt, black_choker, black_jacket, holding, legs, torn_pantyhose, alternate_costume, black_coat, blush, jacket_on_shoulders, long_sleeves, office_lady, shoes, sitting, standing_on_one_leg, tinted_eyewear | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_necktie | military_uniform | solo | looking_at_viewer | white_gloves | white_shirt | black_jacket | smile | upper_body | simple_background | white_background | long_sleeves | closed_mouth | open_mouth | black_skirt | assault_rifle | holding_gun | black_thighhighs | boots | collared_shirt | black_footwear | military_jacket | belt | full_body | peaked_cap | standing | black_pantyhose | eyewear_on_head | high_heels | sunglasses | medium_breasts | pencil_skirt | black_choker | holding | legs | torn_pantyhose | alternate_costume | black_coat | blush | jacket_on_shoulders | office_lady | shoes | sitting | standing_on_one_leg | tinted_eyewear | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:-------------------|:-------|:--------------------|:---------------|:--------------|:---------------|:--------|:-------------|:--------------------|:-------------------|:---------------|:---------------|:-------------|:--------------|:----------------|:--------------|:-------------------|:--------|:-----------------|:-----------------|:------------------|:-------|:------------|:-------------|:-----------|:------------------|:------------------|:-------------|:-------------|:-----------------|:---------------|:---------------|:----------|:-------|:-----------------|:--------------------|:-------------|:--------|:----------------------|:--------------|:--------|:----------|:----------------------|:-----------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 15 | ![](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 | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](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 | X | X | X | X |

huggingartists/bob-dylan

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/bob-dylan" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 2.91167 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/22306423b6ad8777d1ed5b33ad8b0d0b.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/bob-dylan"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Bob Dylan</div> <a href="https://genius.com/artists/bob-dylan"> <div style="text-align: center; font-size: 14px;">@bob-dylan</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/bob-dylan). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/bob-dylan") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |2241| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/bob-dylan") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

AdapterOcean/med_alpaca_standardized_cluster_30_std

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 20960368 num_examples: 32918 download_size: 10266852 dataset_size: 20960368 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_30_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nlp-with-deeplearning/ko.SHP

--- license: cc-by-nc-sa-4.0 task_categories: - text-generation - question-answering tags: - human feedback - rlhf - preferences - reddit - preference model - RL - NLG - evaluation size_categories: - 100K<n<1M language: - ko - en --- # 🚢 Korean Stanford Human Preferences Dataset (Ko.SHP) 이 데이터셋은 자체 구축한 번역기를 활용하여 [stanfordnlp/SHP](https://huggingface.co/datasets/stanfordnlp/SHP) 데이터셋을 번역한 것입니다. 아래의 내용은 해당 번역기로 README 파일을 번역한 것입니다. 참고 부탁드립니다. **If you mention this dataset in a paper, please cite the paper:** [Understanding Dataset Difficulty with V-Usable Information (ICML 2022)](https://proceedings.mlr.press/v162/ethayarajh22a.html). ## Summary SHP는 요리에서 법률 조언에 이르기까지 18가지 다른 주제 영역의 질문/지침에 대한 응답에 대한 **385K 집단 인간 선호도** 데이터 세트이다. 기본 설정은 다른 응답에 대 한 한 응답의 유용성을 반영 하기 위한 것이며 RLHF 보상 모델 및 NLG 평가 모델 (예: [SteamSHP](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl))을 훈련 하는 데 사용 하도록 설정 됩니다. 각각의 예는 질문/지시 및 그 게시물에 대한 한 쌍의 최상위 코멘트를 갖는 레딧 게시물이며, 여기서 하나의 코멘트는 (종합적으로) 레딧 사용자에 의해 더 선호된다. SHP는 댓글 A가 댓글 B 뒤에 작성되었지만 그럼에도 불구하고 점수가 더 높으면 표면적으로는 A가 B보다 더 선호된다는 사실을 이용한다. A가 B보다 먼저 작성되었으면 더 높은 점수가 더 많은 가시성의 결과일 수 있기 때문에 결론을 내릴 수 없었다. 우리는 선호도 라벨이 어떤 반응이 덜 *유해*하기보다는 더 *도움이* 되는지를 반영하기 위한 데이터를 선택했으며 후자는 많은 과거 작업의 초점이다. SHP는 [Anthropic의 HH-RLHF 데이터 세트](https://huggingface.co/datasets/Anthropic/hh-rlhf)와 어떻게 다른가요? 특히, SHP의 모든 데이터는 자연적으로 발생하고 인간이 작성하는 반면 HH-RLHF의 응답은 기계 작성되어 서로를 보완할 수 있는 두 가지 매우 다른 분포를 제공한다. | Dataset | Size | Input | Label | Domains | Data Format | Length | | -------------------- | ---- | -------------------------- | ---------------------------- | ------------------------- | ------------------------------------- | --------------- | | SHP | 385K | 자연 발생 인간 작성 응답 | Collective Human Preference | 18 (labelled) | Question/Instruction + Response (Single-turn) | 최대 10.1K T5 토큰 | | HH-RLHF | 91K | LLM과의 대화 | 개별 인간 선호도 | 레이블이 지정되지 않음 | Live Chat (Multi-turn) | 최대 1.5K T5 토큰 | SHP는 [ELI5](https://huggingface.co/datasets/eli5#source-data)와 같이 Reddit을 스크래핑한 다른 데이터 세트와 어떻게 다른가요? SHP는 타임스탬프 정보를 사용 하 여 선호도를 유추 하는 반면 ELI5는 주석 및 점수만 제공 합니다. 후자는 이전에 만든 주석이 더 많은 가시성에서 더 높은 점수를 얻는 경향이 있기 때문에 선호도를 유추 하기에 충분하지 않습니다. 또한 더 많은 도메인의 데이터를 포함합니다. | Dataset | Size | Comments + Scores | Preferences | Number of Domains | | -------------------- | ---- | ------------------ | -------------| ------------------ | | SHP | 385K | Yes | Yes | 18 | | ELI5 | 270K | Yes | No | 3 | ## 데이터 구조 각 하위 레딧에 대해 하나씩 18개의 디렉터리가 있으며 각 디렉터리에는 학습, 유효성 검사 및 테스트 데이터를 위한 JSONL 파일이 포함되어 있습니다. Huggingface의 `datasets` 라이브러리를 사용하여 데이터를 가져오는 방법은 다음과 같습니다. ```python from datasets import load_dataset # Load all the data dataset = load_dataset("stanfordnlp/shp") # Load one of the subreddits dataset = load_dataset("stanfordnlp/shp", data_dir="askculinary") ``` 다음은 `askculinary/train.json`의 예입니다. ``` { `post_id`:"qt3nxl", `domain`:"askculinary_train", `upvote_ratio`:0.98, `history`:"라즈베리를 분해하는 가장 좋은 방법은 무엇입니까? 이와 같이, 그러나 개별 씨앗까지: https:\/\/i.imgur.com\/Z0c6ZKE.jpg 핀셋으로 분리해 왔는데 시간이 많이 걸립니다. 이번 주말까지 약 10파운드가 소요됩니다. `c_root_id_A`:"hkh25sc", `c_root_id_B`:"hkh25lp", `created_at_utc_A`:1636822112, `created_at_utc_B`:1636822110, `score_A`:340, `score_B`:166, `human_ref_A`:"Pectinex, 아마도? 셀룰로오스를 분해하는 효소입니다. citrus를 사용하면 pectinex의 묽은 용액에 밤새 앉아 결합 조직을 분해하게 됩니다. 결국 완벽한 citrus supremes가 됩니다. 라즈베리를 더 짧은 시간 동안 앉게 되면 같은 방식으로 종자를 분리할 수 있는지 궁금합니다. 여기 예제가 있습니다. https:\/\/www.chefsteps.com\/activities\/perfect-citrus-supreme", `human_ref_B`:"라즈베리 주스는 처음에는 밝은 얼룩을 만들지만 몇 주 후면 거의 아무것도 사라지기 시작합니다. 천연 염료 세계에서 탈주 염료로 알려진 것은 세탁이나 빛에 노출되지 않아도 사라질 것입니다. 그녀가 드레스에 이 얼룩의 멋진 사진을 많이 얻기를 바랍니다. 곧 그것이 그녀가 남긴 전부이기 때문입니다." `labels`:1, `seconds_difference`:2.0, `score_ratio`:2.0481927711 } ``` 상기 필드들은: - ```post_id```: the ID of the Reddit post (string) - ```domain```: subreddit and split the example is drawn from, separated by underscore (string) - ```upvote_ratio```: 긍정 (일명 upvotes) (float) 게시물에서 받은 투표 비율입니다. - ```history```: Post title concatented to post body (string) - ```c_root_id_A```: comment A의 ID (string) - ```c_root_id_B```: comment B (string)의 ID - ```created_at_utc_A```: utc timestamp of when comment A is created (integer) - ```created_at_utc_B```: utc timestamp of when comment B is created (integer) - ```score_A```: (# positive votes - # negative votes + 1) received by comment A (integer) - ```score_B```: (# positive votes - # negative votes + 1) received by comment B (integer) - ```human_ref_A```: comment A의 텍스트 (string) - ```human_ref_B```: comment B의 텍스트 (string) - ```labels```: 선호도 레이블 -- A가 B보다 선호되는 경우 1이고, B가 A보다 선호되는 경우 0입니다. 레이블 분포가 대략 50/50이 되도록 무작위화되었습니다. (정수) - ```seconds_difference```: 덜 선호되는 코멘트가 생성된 후 몇 초 후에 더 선호되는 코멘트가 생성되었는지(항상 >= 0일 것임) (정수) - ```score_ratio```: 더 선호하는 댓글의 점수와 덜 선호하는 댓글의 점수의 비율 (>= 1) (float) ## Dataset Design ### 도메인 선택 데이터는 *서브레딧* 이라는 토픽별 포라로 구성된 공개 포럼인 Reddit에서 공급됩니다. 예를 들어 `askculinary` 하위 레딧은 사용자가 요리 관련 질문을 하고 다른 사용자에 의해 응답 되는 것입니다. SHP에는 18개의 다른 하위 레딧에서 긁어낸 주석에 대한 열차, 유효성 검사 및 테스트 분할이 포함되어 있습니다. 우리는 다음을 기반으로 하위 레딧을 선택했습니다. 1. 잘 알려진 것인지 여부(가입자수 >= 100K) 2. 게시물이 질문 또는 지시를 내릴 것으로 예상되었는지 여부 3. 응답이 얼마나 *도움이* 되는지에 따라 평가되는지 여부 4. 코멘트가 전적으로 개인 경험에 대한 것이 아니라 일부 객관성에 뿌리를 두어야 하는지 여부(예: `askscience` 대 `AskAmericans`)입니다. 열차/검증/테스트 분할은 하위 레딧의 포스트 ID를 각각 90%/5%/5% 비율로 분할하여 생성되어 여러 분할에 포스트가 나타나지 않는다. 상이한 게시물들은 상이한 수의 코멘트들을 갖기 때문에, 각각의 분할에서의 선호들의 수는 정확히 90%/5%/5%가 아니다: | subreddit | train | validation | test | total | | ------------------ | -------: | ---------: | ---: | ----: | | askacademia | 31450 | 2095 | 1708 | 35253 | | askanthropology | 3910 | 203 | 268 | 4381 | | askbaking | 44007 | 2096 | 1544 | 47647 | | askcarguys | 3227 | 159 | 117 | 3503 | | askculinary | 45710 | 2094 | 2563 | 50367 | | askdocs | 6449 | 315 | 455 | 7219 | | askengineers | 57096 | 3154 | 2638 | 62888 | | askhistorians | 3264 | 113 | 164 | 3541 | | askhr | 8295 | 641 | 395 | 9331 | | askphilosophy | 10307 | 608 | 677 | 11592 | | askphysics | 7364 | 409 | 587 | 8360 | | askscience | 13316 | 899 | 977 | 15192 | | asksciencefiction | 29382 | 1576 | 1987 | 32945 | | asksocialscience | 2706 | 147 | 188 | 3041 | | askvet | 3300 | 170 | 224 | 3694 | | changemyview | 38173 | 1637 | 1836 | 41646 | | explainlikeimfive | 19592 | 1014 | 1070 | 21676 | | legaladvice | 21170 | 1106 | 1011 | 23287 | | ALL | 348718 | 18436 | 18409 | 385563 | ### 데이터 선택 포스트/댓글의 점수는 1에 사용자로부터의 상향 투표 수(승인)를 곱하고 하향 투표 수(승인 취소)를 뺀 값입니다. 점수의 값은 상대적입니다. 트래픽이 많은 하위 레딧(게시물)에서는 점수가 높은 게시물(댓글)이 더 많습니다. 게시물에서 더 일찍 게시된 댓글은 단순히 노출이 많아 점수가 더 높은 경향이 있을 것이므로 선호도를 추론할 때 타임스탬프 정보를 사용하는 것이 필수적이다. 게시물 P와 두 개의 주석(A,B)이 주어지면 데이터 세트에 선호도 A > B만 포함했다. 1. A는 *늦지 않게* B로 작성되었고 A는 B보다 높은 점수를 갖는다. 2. 게시물은 2023년 이전에 만들어진 셀프-포스트(즉, 텍스트의 본문이고 다른 페이지로의 링크가 아님)이며, 편집되지 않았으며, NSFW(18 초과)가 아니다. 3. 삭제된 사용자, 사회자 또는 게시물 작성자에 의해 어떠한 코멘트도 이루어지지 않았다. 게시물은 삭제된 사용자 또는 진행자가 만들지 않았습니다. 4. 게시물은 점수가 >=10이고 각 코멘트는 점수가 >=2(적어도 한 번 이상 투표)이다. 주석이 있는 게시물은 `n` 데이터에서 최대 (`n` `2`) 환경 설정을 선택할 수 있습니다. 게시물당 댓글 수는 파레토 배포이기 때문에 상대적으로 적은 수의 게시물이 데이터를 지배하는 것을 방지하기 위해 게시물당 50개의 댓글으로 스크래핑을 제한했다. 이는 위의 모든 기준을 충족해야 하기 때문에 실제로는 훨씬 적은 수이지만 각 게시물에 데이터 집합에서 최대 (`50` `2`를 선택) 주석이 있을 수 있음을 의미 합니다. 레드딧은 서브레드딧마다 상위 1000개 이상의 게시물을 얻는 것을 매우 어렵게 만든다. 최상위 1,000개의 게시물부터 시작하여 Reddit의 검색 기능을 사용하여 각 게시물과 가장 유사한 25개의 게시물을 검색하여 하위 레딧당 최대 7500개의 고유한 게시물 ID를 얻었다. ### 전처리 전처리를 최소한으로 유지하려고 노력했습니다. 서브레디트-특정 약어는 확장되었다(예를 들어, "CMV"를 "내 견해를 변경"으로). 하이퍼링크에서, 참조 텍스트만이 유지되고 URL이 제거되었다(URL이 기입된 경우, 그것은 유지되었다). ## 기본 설정 모델 만들기 ### Finetuning 인간 선호도(예를 들어, NLG 평가 또는 RLHF 보상 모델에 대해)를 예측하기 위해 모델을 피니튜닝하고자 하는 경우, 여기 몇 가지 유용한 팁이 있다: 1. **데이터를 전처리합니다.* * 총 입력 길이는 모델의 토큰 제한 (일반적으로 512 토큰)에 적합 해야 합니다. FLAN-T5와 같은 모델은 위치 임베딩을 사용하지만 512개 토큰 이상의 입력에서 손실을 조정하면 손실이 수렴하지 않는다는 것을 발견했다. 이를 방지 하려면 게시글 텍스트 (`history` 필드에서)를 가능한 한 잘라서 전체 입력이 512 토큰 아래에 있도록 합니다 (그러나 주석을 잘리지 않음). 여전히 512 토큰 이상이면 예제를 건너뜁니다. 2. **충분히 큰 모델을 사용** 합니다. 모든 트레이닝 데이터에 걸쳐 단일 FLAN-T5-xl 모델을 피니튜닝하는 것은 72-73%(전체 입력이 토큰 한계 내에 맞는 예시의 모든 도메인에 걸쳐) 사이의 테스트 정확도를 제공해야 하며, 개별 서브레딧의 경우 65-80% 범위이다. 3. **도메인 내 예측을 수행 합니다.* * 하위 레딧이 관련이 없는 경우 도메인 외 성능이 좋지 않습니다 (예: 환경 설정을 미세 조정 하 고 환경 설정을 테스트 하는 경우 `askculinary` `askcarguys`). 4. **더 적은 에폭에 대해 훈련** InstructGPT 종이 페이퍼는 1 에폭에 대해서만 보상 모델을 훈련하는 것을 제안합니다. 동일한 코멘트가 여러 선호도에서 나타나기 때문에 데이터에 과적합되기 쉽다. 5. **더 적은 데이터에 대한 교육이 도움이 될 수 있습니다* *. 큰 `score_ratio`를 사용하는 환경 설정(예: 주석 B의 점수가 2배인 주석 A)은 모델을 조정하기 위한 더 강력한 신호를 제공하므로 특정 이상의 환경 설정만 고려하려는 것일 수 있습니다 `score_ratio`. 게시물당 선호도 수는 Pareto-distributed이므로 모델이 특정 게시물에 과도하게 적합 하는 것을 방지 하기 위해 특정 게시물에서 선호도 수를 제한 하는 것이 좋습니다. ### 평가 약한 기본 설정보다 강력한 기본 설정을 예측하는 것이 더 쉽기 때문에 단일 정확도 값을 보고하는 대신 성능 곡선을 `score_ratio`의 함수로 보고하는 것이 좋습니다. 예를 들어, 여기 위의 제안들을 사용하여 질문적 데이터에 대해 트레이닝된 FLAN-T5-xl 모델에 대한 정확도 곡선이 있다. 주황색 라인은 2+ 스코어 비율을 갖는 선호도에만 피니튜닝하고 과적합을 방지하기 위해 각 포스트로부터 5개 이하의 선호도를 사용하는 것이다: 로 이루어진 군에서 선택되는 어느 하나인 것을 특징으로 하는 유기 발광 표시 장치. [그래프](curve.png) 우리는 더 낮지만 더 높은 품질의 데이터를 미세 조정하는 것이 실제 단점이 없는 점수 비율이 3.5 미만인 테스트 데이터에 대한 더 높은 정확도로 이어진다는 것을 알 수 있다! 토큰 제한 내에 입력이 맞지 않는 예는 모델에서 처리할 것으로 예상할 수 없기 때문에 실험에서 제외되었다. ### SteamSHP - Open-Source Preference Model 우리는 SHP 데이터 세트와 Anthropic의 HH-RLHF의 도움 데이터 모두에 대해 두 개의 FLAN-T5 모델을 미세 조정했다. 그들은 - 테스트 데이터에서 72.8%를 달성하는 3B 매개 변수 모델인 [SteamSHP-XL](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl)입니다. - 테스트 데이터에서 72.0%를 달성하는 780M 매개 변수 모델인 [SteamSHP-Large](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-large)입니다. NLG 평가, RLHF에 대한 보상 모델 구축 또는 적합하다고 생각하는 다른 목적으로 스팀SHP를 사용하는 것이 좋습니다! ## 편향 및 제한 사항 ### Biases NSFW(18세 이상) 콘텐츠로 게시물을 걸러내고, 잘 조정되고 괴롭힘과 편협에 대한 정책이 있는 하위 레딧을 선택했지만 일부 데이터에는 차별적이거나 해로운 언어가 포함될 수 있다. 데이터는 데이터 세트 작성자의 보기를 반영하지 않습니다. 이러한 하위 레딧의 레딧 사용자도 광범위한 모집단을 대표하지 않는다. 하위 레딧별 인구 통계 정보는 사용할 수 없지만 전체 레딧 사용자는 불균형적으로 남성이며 선진국, 서양 및 영어 사용 국가에서 왔습니다 ([Pew Research](https://www.pewresearch.org/internet/2013/07/03/6-of-online-adults-are-reddit-users/)). 이 데이터에 대해 학습된 모델을 사용하기 전에 이 점을 염두에 두십시오. ### 제한 사항 SHP의 선호도 레이블은 지시/질문이 주어졌을 때 한 응답이 다른 응답과 얼마나 *도움이* 되는지 반영 하기 위한 것입니다. SHP는 좋은 독성 검출기를 배우는 데 필요한 독성 함량을 포함하도록 설계되지 않았기 때문에 위해 최소화에서 사용하기 위한 것이 아니다. 환경 설정 레이블이 더 적은 해를 나타내는 데이터를 찾는 경우 [Anthropic의 HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf)의 유해성 분할을 권장합니다. 또 다른 한계는 SHP에서 선호되는 응답이 반드시 더 사실적인 응답은 아니라는 것이다. 일부 논평은 그들의 반응을 정당화하기 위해 인용을 제공하지만 대부분은 그렇지 않다. 여기에는 `askhistorians` 하위 레딧과 같은 예외가 있으며, 이는 크게 조정되며 답변이 인용을 제공할 것으로 예상된다. SHP의 집단 선호도 라벨은 가중치가 없는 합계를 취하기 전에 사용자에게 각 코멘트에 독립적으로 투표하도록 요청하면 반드시 얻을 수 있는 것은 아니다. Reddit에 대한 주석 점수는 공개적이며 사용자 환경 설정에 영향을 미치는 것으로 알려져 있기 때문입니다. 높은 점수는 [(Muchnik et al., 2013)](https://pubmed.ncbi.nlm.nih.gov/23929980/)보다 긍정적인 표를 얻을 가능성을 높입니다. 이 "허딩 효과"가 사용자의 선호도를 일시적으로 또는 영구적으로 이동시키는지 여부는 불분명하다. 따라서, SHP가 집단적 인간 선호도를 반영하지만, SHP에 대해 훈련된 모델은 개별 선호도가 다르게 집계되는 설정으로 일반화되지 않을 수 있다(예를 들어, 사용자는 현재 코멘트 점수를 전혀 보지 않고 독립적으로 투표하고, 사용자는 부여 후 투표 등). 그렉 스토다드가 지적해줘서 고마워요 ## License Last updated: 03/01/2023 이 데이터 세트는 Reddit과 직접 통신 또는 서면 동의 없이 [Reddit API 사용 약관](https://docs.google.com/a/reddit.com/forms/d/e/1FAIpQLSezNdDNK1-P8mspSbmtC2r86Ee9ZRbC66u929cG2GX0T9UMyw/viewform)에 따라 Reddit을 스크래핑하여 만들었습니다. 사용 약관에 따라 "사용자 콘텐츠"는 Reddit이 아닌 사용자 자신이 소유하고 있으며 Reddit은 "사용자 콘텐츠를 복사 및 표시 하기 위해 독점적이지 않고 양도할 수 없으며 공개되지 않으며 취소할 수 있는 라이선스"를 부여 합니다. Reddit을 스크래핑 하 여 만든 데이터 집합은 연구 커뮤니티에서 널리 사용 됩니다. 예를 들어 Facebook AI 리서치는 Reddit에서 스크래핑 된 데이터를 사용 하 여 라이선스 없이 사용 하도록 만든 2019년 [ELI5](https://huggingface.co/datasets/eli5#source-data) 데이터 집합을 만들었습니다. 인류성 AI는 다른 방법론을 사용 하 여 환경 설정에 대 한 [Reddit을 스크래핑](https://arxiv.org/pdf/2112.00861.pdf) 합니다. 그러나이 데이터는 공개 되지 않았습니다. 정기적인 일정에서 Reddit의 전체 덤프를 사용할 수 있도록 하는 [PushShift Reddit 데이터 세트](https://arxiv.org/abs/2001.08435)도 라이선스 없이 사용할 수 있습니다 (알고 있는 범위). 우리는 책임을 지지 않으며 이 데이터 세트의 다운스트림 사용을 명시적으로 또는 암시적으로 지지하지 않는다. 우리는 향후 어느 시점에서든 SHP 데이터 세트와 이 라이선스를 수정할 수 있는 권한을 보유합니다. ## Contact 데이터에 대한 질문이 있는 경우 kawin@stanford.edu에 문의하십시오. 이 데이터 세트는 카윈 에타야라흐, 하이디(첸유) 장, 이중 왕 및 단 주라프스키에 의해 생성되었다. ## 인용 SHP는 다음 논문에서 제안한 기법을 이용하여 생성하였다. SHP 또는 스팀SHP 모델을 사용하는 경우 이 작업을 인용하십시오. ``` @InProceedings{pmlr-v162-ethayarajh22a, title = {Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information}, author = {Ethayarajh, Kawin and Choi, Yejin and Swayamdipta, Swabha}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {5988--6008}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, } ``` ## 참조 Ethayarajh, K., Choi, Y. & Swayamdipta, S. (2022). Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information. <i>Proceedings of the 39th International Conference on Machine Learning</i>, in <i>Proceedings of Machine Learning Research</i>. 162:5988-6008 Available from https://proceedings.mlr.press/v162/ethayarajh22a.html.

liuyanchen1015/MULTI_VALUE_wnli_linking_relcl

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 3249 num_examples: 17 - name: test num_bytes: 2742 num_examples: 9 - name: train num_bytes: 17600 num_examples: 92 download_size: 18391 dataset_size: 23591 --- # Dataset Card for "MULTI_VALUE_wnli_linking_relcl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

deokhk/zh_wiki_sentences_1000000

--- dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 127836004 num_examples: 1000000 - name: dev num_bytes: 135625 num_examples: 1000 download_size: 88011343 dataset_size: 127971629 --- # Dataset Card for "zh_wiki_sentences_1000000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Birchlabs/openai-prm800k-phase2_train-stepwise-best

--- license: mit ---

open-llm-leaderboard/details_namirocks__student-model-13b-ep3

--- pretty_name: Evaluation run of namirocks/student-model-13b-ep3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [namirocks/student-model-13b-ep3](https://huggingface.co/namirocks/student-model-13b-ep3)\ \ 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_namirocks__student-model-13b-ep3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-30T01:37:20.077989](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__student-model-13b-ep3/blob/main/results_2023-12-30T01-37-20.077989.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\": 0.5621545466705268,\n\ \ \"acc_stderr\": 0.03351539520737431,\n \"acc_norm\": 0.5727655950528345,\n\ \ \"acc_norm_stderr\": 0.03442395762278095,\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871105,\n \"mc2\": 0.35003126952306707,\n\ \ \"mc2_stderr\": 0.014347219852780793\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.43856655290102387,\n \"acc_stderr\": 0.014500682618212865,\n\ \ \"acc_norm\": 0.46501706484641636,\n \"acc_norm_stderr\": 0.01457558392201966\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6061541525592511,\n\ \ \"acc_stderr\": 0.004876028037941937,\n \"acc_norm\": 0.8036247759410476,\n\ \ \"acc_norm_stderr\": 0.003964437012249992\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5855263157894737,\n \"acc_stderr\": 0.04008973785779206,\n\ \ \"acc_norm\": 0.5855263157894737,\n \"acc_norm_stderr\": 0.04008973785779206\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\": 0.6150943396226415,\n \"acc_stderr\": 0.02994649856769995,\n\ \ \"acc_norm\": 0.6150943396226415,\n \"acc_norm_stderr\": 0.02994649856769995\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6041666666666666,\n\ \ \"acc_stderr\": 0.04089465449325582,\n \"acc_norm\": 0.6041666666666666,\n\ \ \"acc_norm_stderr\": 0.04089465449325582\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\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\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\"\ : 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\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\": 0.5664739884393064,\n\ \ \"acc_stderr\": 0.03778621079092056,\n \"acc_norm\": 0.5664739884393064,\n\ \ \"acc_norm_stderr\": 0.03778621079092056\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.04655010411319616,\n\ \ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.04655010411319616\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.43829787234042555,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.43829787234042555,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n\ \ \"acc_stderr\": 0.043036840335373146,\n \"acc_norm\": 0.2982456140350877,\n\ \ \"acc_norm_stderr\": 0.043036840335373146\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\ \ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3412698412698413,\n \"acc_stderr\": 0.024419234966819067,\n \"\ acc_norm\": 0.3412698412698413,\n \"acc_norm_stderr\": 0.024419234966819067\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n\ \ \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n\ \ \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\ : 0.6483870967741936,\n \"acc_stderr\": 0.02716253782694846,\n \"\ acc_norm\": 0.6483870967741936,\n \"acc_norm_stderr\": 0.02716253782694846\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4975369458128079,\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\": 0.57,\n \"acc_stderr\": 0.04975698519562427,\n \"acc_norm\"\ : 0.57,\n \"acc_norm_stderr\": 0.04975698519562427\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.035679697722680495,\n\ \ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.035679697722680495\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6919191919191919,\n \"acc_stderr\": 0.03289477330098617,\n \"\ acc_norm\": 0.6919191919191919,\n \"acc_norm_stderr\": 0.03289477330098617\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8082901554404145,\n \"acc_stderr\": 0.028408953626245265,\n\ \ \"acc_norm\": 0.8082901554404145,\n \"acc_norm_stderr\": 0.028408953626245265\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5846153846153846,\n \"acc_stderr\": 0.02498535492310233,\n \ \ \"acc_norm\": 0.5846153846153846,\n \"acc_norm_stderr\": 0.02498535492310233\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948496,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948496\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6008403361344538,\n \"acc_stderr\": 0.03181110032413926,\n \ \ \"acc_norm\": 0.6008403361344538,\n \"acc_norm_stderr\": 0.03181110032413926\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\"\ : 0.7559633027522936,\n \"acc_stderr\": 0.0184152863514164,\n \"acc_norm\"\ : 0.7559633027522936,\n \"acc_norm_stderr\": 0.0184152863514164\n },\n\ \ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.03388857118502326,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.03388857118502326\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501947,\n\ \ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501947\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7552742616033755,\n \"acc_stderr\": 0.02798569938703642,\n \ \ \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.02798569938703642\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6870229007633588,\n \"acc_stderr\": 0.04066962905677698,\n\ \ \"acc_norm\": 0.6870229007633588,\n \"acc_norm_stderr\": 0.04066962905677698\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.03680350371286461,\n\ \ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.03680350371286461\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n\ \ \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.4017857142857143,\n\ \ \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.024414947304543678,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.024414947304543678\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7535121328224776,\n\ \ \"acc_stderr\": 0.015411308769686936,\n \"acc_norm\": 0.7535121328224776,\n\ \ \"acc_norm_stderr\": 0.015411308769686936\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.026296227915613663,\n\ \ \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.026296227915613663\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33519553072625696,\n\ \ \"acc_stderr\": 0.01578800719018588,\n \"acc_norm\": 0.33519553072625696,\n\ \ \"acc_norm_stderr\": 0.01578800719018588\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6339869281045751,\n \"acc_stderr\": 0.027582811415159617,\n\ \ \"acc_norm\": 0.6339869281045751,\n \"acc_norm_stderr\": 0.027582811415159617\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6334405144694534,\n\ \ \"acc_stderr\": 0.027368078243971635,\n \"acc_norm\": 0.6334405144694534,\n\ \ \"acc_norm_stderr\": 0.027368078243971635\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.026869490744815247,\n\ \ \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.026869490744815247\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4326241134751773,\n \"acc_stderr\": 0.02955545423677886,\n \ \ \"acc_norm\": 0.4326241134751773,\n \"acc_norm_stderr\": 0.02955545423677886\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.423728813559322,\n\ \ \"acc_stderr\": 0.01262078515588599,\n \"acc_norm\": 0.423728813559322,\n\ \ \"acc_norm_stderr\": 0.01262078515588599\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.03035230339535196,\n\ \ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.03035230339535196\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5816993464052288,\n \"acc_stderr\": 0.019955975145835542,\n \ \ \"acc_norm\": 0.5816993464052288,\n \"acc_norm_stderr\": 0.019955975145835542\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\": 0.6489795918367347,\n \"acc_stderr\": 0.03055531675557364,\n\ \ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.03055531675557364\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7711442786069652,\n\ \ \"acc_stderr\": 0.029705284056772436,\n \"acc_norm\": 0.7711442786069652,\n\ \ \"acc_norm_stderr\": 0.029705284056772436\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338734,\n\ \ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338734\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871105,\n \"mc2\": 0.35003126952306707,\n\ \ \"mc2_stderr\": 0.014347219852780793\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7221783741120757,\n \"acc_stderr\": 0.012588918183871596\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/namirocks/student-model-13b-ep3 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_30T01_37_20.077989 path: - '**/details_harness|arc:challenge|25_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-30T01-37-20.077989.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|gsm8k|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hellaswag|10_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-30T01-37-20.077989.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-management|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T01-37-20.077989.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|truthfulqa:mc|0_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-30T01-37-20.077989.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_30T01_37_20.077989 path: - '**/details_harness|winogrande|5_2023-12-30T01-37-20.077989.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-30T01-37-20.077989.parquet' - config_name: results data_files: - split: 2023_12_30T01_37_20.077989 path: - results_2023-12-30T01-37-20.077989.parquet - split: latest path: - results_2023-12-30T01-37-20.077989.parquet --- # Dataset Card for Evaluation run of namirocks/student-model-13b-ep3  Dataset automatically created during the evaluation run of model [namirocks/student-model-13b-ep3](https://huggingface.co/namirocks/student-model-13b-ep3) 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_namirocks__student-model-13b-ep3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-30T01:37:20.077989](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__student-model-13b-ep3/blob/main/results_2023-12-30T01-37-20.077989.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": 0.5621545466705268, "acc_stderr": 0.03351539520737431, "acc_norm": 0.5727655950528345, "acc_norm_stderr": 0.03442395762278095, "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871105, "mc2": 0.35003126952306707, "mc2_stderr": 0.014347219852780793 }, "harness|arc:challenge|25": { "acc": 0.43856655290102387, "acc_stderr": 0.014500682618212865, "acc_norm": 0.46501706484641636, "acc_norm_stderr": 0.01457558392201966 }, "harness|hellaswag|10": { "acc": 0.6061541525592511, "acc_stderr": 0.004876028037941937, "acc_norm": 0.8036247759410476, "acc_norm_stderr": 0.003964437012249992 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5855263157894737, "acc_stderr": 0.04008973785779206, "acc_norm": 0.5855263157894737, "acc_norm_stderr": 0.04008973785779206 }, "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": 0.6150943396226415, "acc_stderr": 0.02994649856769995, "acc_norm": 0.6150943396226415, "acc_norm_stderr": 0.02994649856769995 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6041666666666666, "acc_stderr": 0.04089465449325582, "acc_norm": 0.6041666666666666, "acc_norm_stderr": 0.04089465449325582 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "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": 0.5664739884393064, "acc_stderr": 0.03778621079092056, "acc_norm": 0.5664739884393064, "acc_norm_stderr": 0.03778621079092056 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3235294117647059, "acc_stderr": 0.04655010411319616, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.04655010411319616 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.43829787234042555, "acc_stderr": 0.03243618636108102, "acc_norm": 0.43829787234042555, "acc_norm_stderr": 0.03243618636108102 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2982456140350877, "acc_stderr": 0.043036840335373146, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.043036840335373146 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.503448275862069, "acc_stderr": 0.04166567577101579, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.04166567577101579 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3412698412698413, "acc_stderr": 0.024419234966819067, "acc_norm": 0.3412698412698413, "acc_norm_stderr": 0.024419234966819067 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.36507936507936506, "acc_stderr": 0.04306241259127153, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.04306241259127153 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6483870967741936, "acc_stderr": 0.02716253782694846, "acc_norm": 0.6483870967741936, "acc_norm_stderr": 0.02716253782694846 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.57, "acc_stderr": 0.04975698519562427, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562427 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.703030303030303, "acc_stderr": 0.035679697722680495, "acc_norm": 0.703030303030303, "acc_norm_stderr": 0.035679697722680495 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6919191919191919, "acc_stderr": 0.03289477330098617, "acc_norm": 0.6919191919191919, "acc_norm_stderr": 0.03289477330098617 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8082901554404145, "acc_stderr": 0.028408953626245265, "acc_norm": 0.8082901554404145, "acc_norm_stderr": 0.028408953626245265 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5846153846153846, "acc_stderr": 0.02498535492310233, "acc_norm": 0.5846153846153846, "acc_norm_stderr": 0.02498535492310233 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948496, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948496 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6008403361344538, "acc_stderr": 0.03181110032413926, "acc_norm": 0.6008403361344538, "acc_norm_stderr": 0.03181110032413926 }, "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": 0.7559633027522936, "acc_stderr": 0.0184152863514164, "acc_norm": 0.7559633027522936, "acc_norm_stderr": 0.0184152863514164 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03388857118502326, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03388857118502326 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501947, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501947 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7552742616033755, "acc_stderr": 0.02798569938703642, "acc_norm": 0.7552742616033755, "acc_norm_stderr": 0.02798569938703642 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.031493846709941306, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.031493846709941306 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6870229007633588, "acc_stderr": 0.04066962905677698, "acc_norm": 0.6870229007633588, "acc_norm_stderr": 0.04066962905677698 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6748466257668712, "acc_stderr": 0.03680350371286461, "acc_norm": 0.6748466257668712, "acc_norm_stderr": 0.03680350371286461 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4017857142857143, "acc_stderr": 0.04653333146973646, "acc_norm": 0.4017857142857143, "acc_norm_stderr": 0.04653333146973646 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8333333333333334, "acc_stderr": 0.024414947304543678, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.024414947304543678 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7535121328224776, "acc_stderr": 0.015411308769686936, "acc_norm": 0.7535121328224776, "acc_norm_stderr": 0.015411308769686936 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6069364161849711, "acc_stderr": 0.026296227915613663, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.026296227915613663 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.33519553072625696, "acc_stderr": 0.01578800719018588, "acc_norm": 0.33519553072625696, "acc_norm_stderr": 0.01578800719018588 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6339869281045751, "acc_stderr": 0.027582811415159617, "acc_norm": 0.6339869281045751, "acc_norm_stderr": 0.027582811415159617 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6334405144694534, "acc_stderr": 0.027368078243971635, "acc_norm": 0.6334405144694534, "acc_norm_stderr": 0.027368078243971635 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6296296296296297, "acc_stderr": 0.026869490744815247, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.026869490744815247 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4326241134751773, "acc_stderr": 0.02955545423677886, "acc_norm": 0.4326241134751773, "acc_norm_stderr": 0.02955545423677886 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.423728813559322, "acc_stderr": 0.01262078515588599, "acc_norm": 0.423728813559322, "acc_norm_stderr": 0.01262078515588599 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5183823529411765, "acc_stderr": 0.03035230339535196, "acc_norm": 0.5183823529411765, "acc_norm_stderr": 0.03035230339535196 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5816993464052288, "acc_stderr": 0.019955975145835542, "acc_norm": 0.5816993464052288, "acc_norm_stderr": 0.019955975145835542 }, "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": 0.6489795918367347, "acc_stderr": 0.03055531675557364, "acc_norm": 0.6489795918367347, "acc_norm_stderr": 0.03055531675557364 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7711442786069652, "acc_stderr": 0.029705284056772436, "acc_norm": 0.7711442786069652, "acc_norm_stderr": 0.029705284056772436 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.4578313253012048, "acc_stderr": 0.038786267710023595, "acc_norm": 0.4578313253012048, "acc_norm_stderr": 0.038786267710023595 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.783625730994152, "acc_stderr": 0.03158149539338734, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.03158149539338734 }, "harness|truthfulqa:mc|0": { "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871105, "mc2": 0.35003126952306707, "mc2_stderr": 0.014347219852780793 }, "harness|winogrande|5": { "acc": 0.7221783741120757, "acc_stderr": 0.012588918183871596 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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]

tyzhu/squad_wrong_title_v3_train_10_eval_10

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 276687 num_examples: 184 - name: validation num_bytes: 64754 num_examples: 68 download_size: 71442 dataset_size: 341441 --- # Dataset Card for "squad_wrong_title_v3_train_10_eval_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

divers/requirement-question

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: index dtype: int64 - name: job_requirement dtype: string - name: questions dtype: string splits: - name: train num_bytes: 35480682 num_examples: 23237 download_size: 4168927 dataset_size: 35480682 --- # Dataset Card for "requirement-question" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Kaue123456/JoaoGriloMatheusNachtergaele

--- license: openrail ---

arthurmluz/cstnews_data-temario_results

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 69932 num_examples: 16 download_size: 0 dataset_size: 69932 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "cstnews_data-temario_results" rouge= {'rouge1': 0.5207584715132082, 'rouge2': 0.34711381882009107, 'rougeL': 0.38095639884621346, 'rougeLsum': 0.38095639884621346} bert= {'precision': 0.7428307943046093, 'recall': 0.8364794515073299, 'f1': 0.7866528294980526} mover = 0.6287250343090405

open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3

--- pretty_name: Evaluation run of OpenAssistant/stablelm-7b-sft-v7-epoch-3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [OpenAssistant/stablelm-7b-sft-v7-epoch-3](https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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 agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-13T03:23:25.661445](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3/blob/main/results_2023-10-13T03-23-25.661445.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.05578859060402685,\n\ \ \"em_stderr\": 0.0023504280872280073,\n \"f1\": 0.10613569630872476,\n\ \ \"f1_stderr\": 0.0026144580255279513,\n \"acc\": 0.27616530425036784,\n\ \ \"acc_stderr\": 0.007839405520583978\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.05578859060402685,\n \"em_stderr\": 0.0023504280872280073,\n\ \ \"f1\": 0.10613569630872476,\n \"f1_stderr\": 0.0026144580255279513\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \ \ \"acc_stderr\": 0.0016927007401501943\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5485398579321231,\n \"acc_stderr\": 0.01398611030101776\n\ \ }\n}\n```" repo_url: https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3 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_07_19T17_06_42.731727 path: - '**/details_harness|arc:challenge|25_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|arc:challenge|25_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T17:07:54.588127.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_13T03_23_25.661445 path: - '**/details_harness|drop|3_2023-10-13T03-23-25.661445.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-13T03-23-25.661445.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_13T03_23_25.661445 path: - '**/details_harness|gsm8k|5_2023-10-13T03-23-25.661445.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-13T03-23-25.661445.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hellaswag|10_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hellaswag|10_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:06:42.731727.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:07:54.588127.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:07:54.588127.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T17_06_42.731727 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T17:06:42.731727.parquet' - split: 2023_07_19T17_07_54.588127 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T17:07:54.588127.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T17:07:54.588127.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_13T03_23_25.661445 path: - '**/details_harness|winogrande|5_2023-10-13T03-23-25.661445.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-13T03-23-25.661445.parquet' - config_name: results data_files: - split: 2023_07_19T17_06_42.731727 path: - results_2023-07-19T17:06:42.731727.parquet - split: 2023_07_19T17_07_54.588127 path: - results_2023-07-19T17:07:54.588127.parquet - split: 2023_10_13T03_23_25.661445 path: - results_2023-10-13T03-23-25.661445.parquet - split: latest path: - results_2023-10-13T03-23-25.661445.parquet --- # Dataset Card for Evaluation run of OpenAssistant/stablelm-7b-sft-v7-epoch-3 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [OpenAssistant/stablelm-7b-sft-v7-epoch-3](https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-13T03:23:25.661445](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__stablelm-7b-sft-v7-epoch-3/blob/main/results_2023-10-13T03-23-25.661445.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.05578859060402685, "em_stderr": 0.0023504280872280073, "f1": 0.10613569630872476, "f1_stderr": 0.0026144580255279513, "acc": 0.27616530425036784, "acc_stderr": 0.007839405520583978 }, "harness|drop|3": { "em": 0.05578859060402685, "em_stderr": 0.0023504280872280073, "f1": 0.10613569630872476, "f1_stderr": 0.0026144580255279513 }, "harness|gsm8k|5": { "acc": 0.0037907505686125853, "acc_stderr": 0.0016927007401501943 }, "harness|winogrande|5": { "acc": 0.5485398579321231, "acc_stderr": 0.01398611030101776 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

atmallen/neg_companies_azaria_mitchell

--- dataset_info: features: - name: statement dtype: string - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 75670.4 num_examples: 880 - name: test num_bytes: 18917.6 num_examples: 220 download_size: 29413 dataset_size: 94588.0 --- # Dataset Card for "neg_companies_azaria_mitchell" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dongyoung4091/shp-generated_flan_t5_large_with_features

--- dataset_info: features: - name: response dtype: string - name: prompt dtype: string - name: helpfulness dtype: int64 - name: specificity dtype: int64 - name: intent dtype: int64 - name: factuality dtype: int64 - name: easy-to-understand dtype: int64 - name: relevance dtype: int64 - name: readability dtype: int64 - name: enough-detail dtype: int64 - name: 'biased:' dtype: int64 - name: fail-to-consider-individual-preferences dtype: int64 - name: repetetive dtype: int64 - name: fail-to-consider-context dtype: int64 - name: too-long dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1736538 num_examples: 1500 download_size: 215337 dataset_size: 1736538 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "shp-generated_flan_t5_large_with_features" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_yyh0901__lloma_step200

--- pretty_name: Evaluation run of yyh0901/lloma_step200 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [yyh0901/lloma_step200](https://huggingface.co/yyh0901/lloma_step200) 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_yyh0901__lloma_step200\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-06T13:05:40.214845](https://huggingface.co/datasets/open-llm-leaderboard/details_yyh0901__lloma_step200/blob/main/results_2024-04-06T13-05-40.214845.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\": 0.4370638206369057,\n\ \ \"acc_stderr\": 0.034251250239833685,\n \"acc_norm\": 0.44330275084620024,\n\ \ \"acc_norm_stderr\": 0.035090552757218396,\n \"mc1\": 0.23378212974296206,\n\ \ \"mc1_stderr\": 0.014816195991931578,\n \"mc2\": 0.398074913504716,\n\ \ \"mc2_stderr\": 0.01370017096726305\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.46245733788395904,\n \"acc_stderr\": 0.01457014449507558,\n\ \ \"acc_norm\": 0.5068259385665529,\n \"acc_norm_stderr\": 0.014610029151379813\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.571400119498108,\n\ \ \"acc_stderr\": 0.004938643787869547,\n \"acc_norm\": 0.7714598685520813,\n\ \ \"acc_norm_stderr\": 0.004190341541141985\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\": 0.42962962962962964,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.4342105263157895,\n \"acc_stderr\": 0.0403356566784832,\n\ \ \"acc_norm\": 0.4342105263157895,\n \"acc_norm_stderr\": 0.0403356566784832\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.44528301886792454,\n \"acc_stderr\": 0.030588052974270658,\n\ \ \"acc_norm\": 0.44528301886792454,\n \"acc_norm_stderr\": 0.030588052974270658\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4166666666666667,\n\ \ \"acc_stderr\": 0.04122728707651282,\n \"acc_norm\": 0.4166666666666667,\n\ \ \"acc_norm_stderr\": 0.04122728707651282\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"\ acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\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\": 0.36416184971098264,\n\ \ \"acc_stderr\": 0.03669072477416908,\n \"acc_norm\": 0.36416184971098264,\n\ \ \"acc_norm_stderr\": 0.03669072477416908\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179964,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179964\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n\ \ \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4425531914893617,\n \"acc_stderr\": 0.032469569197899575,\n\ \ \"acc_norm\": 0.4425531914893617,\n \"acc_norm_stderr\": 0.032469569197899575\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\ \ \"acc_stderr\": 0.04266339443159393,\n \"acc_norm\": 0.2894736842105263,\n\ \ \"acc_norm_stderr\": 0.04266339443159393\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.040824829046386284,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.040824829046386284\n \ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708624,\n \"\ acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708624\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.25396825396825395,\n\ \ \"acc_stderr\": 0.038932596106046734,\n \"acc_norm\": 0.25396825396825395,\n\ \ \"acc_norm_stderr\": 0.038932596106046734\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\": 0.4645161290322581,\n\ \ \"acc_stderr\": 0.028372287797962956,\n \"acc_norm\": 0.4645161290322581,\n\ \ \"acc_norm_stderr\": 0.028372287797962956\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.31527093596059114,\n \"acc_stderr\": 0.03269080871970187,\n\ \ \"acc_norm\": 0.31527093596059114,\n \"acc_norm_stderr\": 0.03269080871970187\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\ : {\n \"acc\": 0.5212121212121212,\n \"acc_stderr\": 0.03900828913737302,\n\ \ \"acc_norm\": 0.5212121212121212,\n \"acc_norm_stderr\": 0.03900828913737302\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.4797979797979798,\n \"acc_stderr\": 0.03559443565563919,\n \"\ acc_norm\": 0.4797979797979798,\n \"acc_norm_stderr\": 0.03559443565563919\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.6424870466321243,\n \"acc_stderr\": 0.034588160421810114,\n\ \ \"acc_norm\": 0.6424870466321243,\n \"acc_norm_stderr\": 0.034588160421810114\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.441025641025641,\n \"acc_stderr\": 0.025174048384000745,\n \ \ \"acc_norm\": 0.441025641025641,\n \"acc_norm_stderr\": 0.025174048384000745\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340496,\n \ \ \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340496\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.031041941304059278,\n\ \ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.031041941304059278\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389023,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389023\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6110091743119266,\n \"acc_stderr\": 0.020902300887392873,\n \"\ acc_norm\": 0.6110091743119266,\n \"acc_norm_stderr\": 0.020902300887392873\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.27314814814814814,\n \"acc_stderr\": 0.03038805130167812,\n \"\ acc_norm\": 0.27314814814814814,\n \"acc_norm_stderr\": 0.03038805130167812\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5245098039215687,\n \"acc_stderr\": 0.03505093194348798,\n \"\ acc_norm\": 0.5245098039215687,\n \"acc_norm_stderr\": 0.03505093194348798\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.5780590717299579,\n \"acc_stderr\": 0.032148146302403695,\n \ \ \"acc_norm\": 0.5780590717299579,\n \"acc_norm_stderr\": 0.032148146302403695\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5201793721973094,\n\ \ \"acc_stderr\": 0.033530461674123005,\n \"acc_norm\": 0.5201793721973094,\n\ \ \"acc_norm_stderr\": 0.033530461674123005\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5419847328244275,\n \"acc_stderr\": 0.04369802690578756,\n\ \ \"acc_norm\": 0.5419847328244275,\n \"acc_norm_stderr\": 0.04369802690578756\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.5950413223140496,\n \"acc_stderr\": 0.04481137755942469,\n \"\ acc_norm\": 0.5950413223140496,\n \"acc_norm_stderr\": 0.04481137755942469\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.49074074074074076,\n\ \ \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.49074074074074076,\n\ \ \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.4294478527607362,\n \"acc_stderr\": 0.038890666191127216,\n\ \ \"acc_norm\": 0.4294478527607362,\n \"acc_norm_stderr\": 0.038890666191127216\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\ \ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\ \ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.3592233009708738,\n \"acc_stderr\": 0.047504583990416946,\n\ \ \"acc_norm\": 0.3592233009708738,\n \"acc_norm_stderr\": 0.047504583990416946\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6623931623931624,\n\ \ \"acc_stderr\": 0.030980296992618558,\n \"acc_norm\": 0.6623931623931624,\n\ \ \"acc_norm_stderr\": 0.030980296992618558\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5887611749680716,\n\ \ \"acc_stderr\": 0.01759597190805657,\n \"acc_norm\": 0.5887611749680716,\n\ \ \"acc_norm_stderr\": 0.01759597190805657\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.4913294797687861,\n \"acc_stderr\": 0.026915047355369804,\n\ \ \"acc_norm\": 0.4913294797687861,\n \"acc_norm_stderr\": 0.026915047355369804\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.42810457516339867,\n \"acc_stderr\": 0.028332397483664274,\n\ \ \"acc_norm\": 0.42810457516339867,\n \"acc_norm_stderr\": 0.028332397483664274\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5401929260450161,\n\ \ \"acc_stderr\": 0.028306190403305696,\n \"acc_norm\": 0.5401929260450161,\n\ \ \"acc_norm_stderr\": 0.028306190403305696\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.027815973433878014,\n\ \ \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.027815973433878014\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.35106382978723405,\n \"acc_stderr\": 0.028473501272963764,\n \ \ \"acc_norm\": 0.35106382978723405,\n \"acc_norm_stderr\": 0.028473501272963764\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3539765319426336,\n\ \ \"acc_stderr\": 0.01221350473173164,\n \"acc_norm\": 0.3539765319426336,\n\ \ \"acc_norm_stderr\": 0.01221350473173164\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.030352303395351964,\n\ \ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.030352303395351964\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4215686274509804,\n \"acc_stderr\": 0.019977422600227467,\n \ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.019977422600227467\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5181818181818182,\n\ \ \"acc_stderr\": 0.04785964010794915,\n \"acc_norm\": 0.5181818181818182,\n\ \ \"acc_norm_stderr\": 0.04785964010794915\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.4204081632653061,\n \"acc_stderr\": 0.03160106993449604,\n\ \ \"acc_norm\": 0.4204081632653061,\n \"acc_norm_stderr\": 0.03160106993449604\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5920398009950248,\n\ \ \"acc_stderr\": 0.03475116365194092,\n \"acc_norm\": 0.5920398009950248,\n\ \ \"acc_norm_stderr\": 0.03475116365194092\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.35542168674698793,\n\ \ \"acc_stderr\": 0.03726214354322415,\n \"acc_norm\": 0.35542168674698793,\n\ \ \"acc_norm_stderr\": 0.03726214354322415\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.6549707602339181,\n \"acc_stderr\": 0.036459813773888065,\n\ \ \"acc_norm\": 0.6549707602339181,\n \"acc_norm_stderr\": 0.036459813773888065\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23378212974296206,\n\ \ \"mc1_stderr\": 0.014816195991931578,\n \"mc2\": 0.398074913504716,\n\ \ \"mc2_stderr\": 0.01370017096726305\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7087608524072613,\n \"acc_stderr\": 0.012769029305370697\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04169825625473844,\n \ \ \"acc_stderr\": 0.005506205058175746\n }\n}\n```" repo_url: https://huggingface.co/yyh0901/lloma_step200 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|arc:challenge|25_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-06T13-05-40.214845.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|gsm8k|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hellaswag|10_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-06T13-05-40.214845.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-management|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T13-05-40.214845.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|truthfulqa:mc|0_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-06T13-05-40.214845.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_06T13_05_40.214845 path: - '**/details_harness|winogrande|5_2024-04-06T13-05-40.214845.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-06T13-05-40.214845.parquet' - config_name: results data_files: - split: 2024_04_06T13_05_40.214845 path: - results_2024-04-06T13-05-40.214845.parquet - split: latest path: - results_2024-04-06T13-05-40.214845.parquet --- # Dataset Card for Evaluation run of yyh0901/lloma_step200  Dataset automatically created during the evaluation run of model [yyh0901/lloma_step200](https://huggingface.co/yyh0901/lloma_step200) 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_yyh0901__lloma_step200", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-06T13:05:40.214845](https://huggingface.co/datasets/open-llm-leaderboard/details_yyh0901__lloma_step200/blob/main/results_2024-04-06T13-05-40.214845.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": 0.4370638206369057, "acc_stderr": 0.034251250239833685, "acc_norm": 0.44330275084620024, "acc_norm_stderr": 0.035090552757218396, "mc1": 0.23378212974296206, "mc1_stderr": 0.014816195991931578, "mc2": 0.398074913504716, "mc2_stderr": 0.01370017096726305 }, "harness|arc:challenge|25": { "acc": 0.46245733788395904, "acc_stderr": 0.01457014449507558, "acc_norm": 0.5068259385665529, "acc_norm_stderr": 0.014610029151379813 }, "harness|hellaswag|10": { "acc": 0.571400119498108, "acc_stderr": 0.004938643787869547, "acc_norm": 0.7714598685520813, "acc_norm_stderr": 0.004190341541141985 }, "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": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4342105263157895, "acc_stderr": 0.0403356566784832, "acc_norm": 0.4342105263157895, "acc_norm_stderr": 0.0403356566784832 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.44528301886792454, "acc_stderr": 0.030588052974270658, "acc_norm": 0.44528301886792454, "acc_norm_stderr": 0.030588052974270658 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4166666666666667, "acc_stderr": 0.04122728707651282, "acc_norm": 0.4166666666666667, "acc_norm_stderr": 0.04122728707651282 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.36416184971098264, "acc_stderr": 0.03669072477416908, "acc_norm": 0.36416184971098264, "acc_norm_stderr": 0.03669072477416908 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179964, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179964 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4425531914893617, "acc_stderr": 0.032469569197899575, "acc_norm": 0.4425531914893617, "acc_norm_stderr": 0.032469569197899575 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159393, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159393 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4, "acc_stderr": 0.040824829046386284, "acc_norm": 0.4, "acc_norm_stderr": 0.040824829046386284 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.26455026455026454, "acc_stderr": 0.022717467897708624, "acc_norm": 0.26455026455026454, "acc_norm_stderr": 0.022717467897708624 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.25396825396825395, "acc_stderr": 0.038932596106046734, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.038932596106046734 }, "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": 0.4645161290322581, "acc_stderr": 0.028372287797962956, "acc_norm": 0.4645161290322581, "acc_norm_stderr": 0.028372287797962956 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.31527093596059114, "acc_stderr": 0.03269080871970187, "acc_norm": 0.31527093596059114, "acc_norm_stderr": 0.03269080871970187 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5212121212121212, "acc_stderr": 0.03900828913737302, "acc_norm": 0.5212121212121212, "acc_norm_stderr": 0.03900828913737302 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.4797979797979798, "acc_stderr": 0.03559443565563919, "acc_norm": 0.4797979797979798, "acc_norm_stderr": 0.03559443565563919 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6424870466321243, "acc_stderr": 0.034588160421810114, "acc_norm": 0.6424870466321243, "acc_norm_stderr": 0.034588160421810114 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.441025641025641, "acc_stderr": 0.025174048384000745, "acc_norm": 0.441025641025641, "acc_norm_stderr": 0.025174048384000745 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2851851851851852, "acc_stderr": 0.027528599210340496, "acc_norm": 0.2851851851851852, "acc_norm_stderr": 0.027528599210340496 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.35294117647058826, "acc_stderr": 0.031041941304059278, "acc_norm": 0.35294117647058826, "acc_norm_stderr": 0.031041941304059278 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389023, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389023 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6110091743119266, "acc_stderr": 0.020902300887392873, "acc_norm": 0.6110091743119266, "acc_norm_stderr": 0.020902300887392873 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.27314814814814814, "acc_stderr": 0.03038805130167812, "acc_norm": 0.27314814814814814, "acc_norm_stderr": 0.03038805130167812 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5245098039215687, "acc_stderr": 0.03505093194348798, "acc_norm": 0.5245098039215687, "acc_norm_stderr": 0.03505093194348798 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5780590717299579, "acc_stderr": 0.032148146302403695, "acc_norm": 0.5780590717299579, "acc_norm_stderr": 0.032148146302403695 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5201793721973094, "acc_stderr": 0.033530461674123005, "acc_norm": 0.5201793721973094, "acc_norm_stderr": 0.033530461674123005 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5419847328244275, "acc_stderr": 0.04369802690578756, "acc_norm": 0.5419847328244275, "acc_norm_stderr": 0.04369802690578756 }, "harness|hendrycksTest-international_law|5": { "acc": 0.5950413223140496, "acc_stderr": 0.04481137755942469, "acc_norm": 0.5950413223140496, "acc_norm_stderr": 0.04481137755942469 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.49074074074074076, "acc_stderr": 0.04832853553437055, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.04832853553437055 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.4294478527607362, "acc_stderr": 0.038890666191127216, "acc_norm": 0.4294478527607362, "acc_norm_stderr": 0.038890666191127216 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness|hendrycksTest-management|5": { "acc": 0.3592233009708738, "acc_stderr": 0.047504583990416946, "acc_norm": 0.3592233009708738, "acc_norm_stderr": 0.047504583990416946 }, "harness|hendrycksTest-marketing|5": { "acc": 0.6623931623931624, "acc_stderr": 0.030980296992618558, "acc_norm": 0.6623931623931624, "acc_norm_stderr": 0.030980296992618558 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5887611749680716, "acc_stderr": 0.01759597190805657, "acc_norm": 0.5887611749680716, "acc_norm_stderr": 0.01759597190805657 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.4913294797687861, "acc_stderr": 0.026915047355369804, "acc_norm": 0.4913294797687861, "acc_norm_stderr": 0.026915047355369804 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.42810457516339867, "acc_stderr": 0.028332397483664274, "acc_norm": 0.42810457516339867, "acc_norm_stderr": 0.028332397483664274 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5401929260450161, "acc_stderr": 0.028306190403305696, "acc_norm": 0.5401929260450161, "acc_norm_stderr": 0.028306190403305696 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.49074074074074076, "acc_stderr": 0.027815973433878014, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.027815973433878014 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.35106382978723405, "acc_stderr": 0.028473501272963764, "acc_norm": 0.35106382978723405, "acc_norm_stderr": 0.028473501272963764 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3539765319426336, "acc_stderr": 0.01221350473173164, "acc_norm": 0.3539765319426336, "acc_norm_stderr": 0.01221350473173164 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5183823529411765, "acc_stderr": 0.030352303395351964, "acc_norm": 0.5183823529411765, "acc_norm_stderr": 0.030352303395351964 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4215686274509804, "acc_stderr": 0.019977422600227467, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.019977422600227467 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5181818181818182, "acc_stderr": 0.04785964010794915, "acc_norm": 0.5181818181818182, "acc_norm_stderr": 0.04785964010794915 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4204081632653061, "acc_stderr": 0.03160106993449604, "acc_norm": 0.4204081632653061, "acc_norm_stderr": 0.03160106993449604 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5920398009950248, "acc_stderr": 0.03475116365194092, "acc_norm": 0.5920398009950248, "acc_norm_stderr": 0.03475116365194092 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-virology|5": { "acc": 0.35542168674698793, "acc_stderr": 0.03726214354322415, "acc_norm": 0.35542168674698793, "acc_norm_stderr": 0.03726214354322415 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6549707602339181, "acc_stderr": 0.036459813773888065, "acc_norm": 0.6549707602339181, "acc_norm_stderr": 0.036459813773888065 }, "harness|truthfulqa:mc|0": { "mc1": 0.23378212974296206, "mc1_stderr": 0.014816195991931578, "mc2": 0.398074913504716, "mc2_stderr": 0.01370017096726305 }, "harness|winogrande|5": { "acc": 0.7087608524072613, "acc_stderr": 0.012769029305370697 }, "harness|gsm8k|5": { "acc": 0.04169825625473844, "acc_stderr": 0.005506205058175746 } } ``` ## 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]

tvergho/audio-diffusion-512

--- dataset_info: features: - name: image dtype: image - name: audio_file dtype: string - name: slice dtype: int16 splits: - name: train num_bytes: 896860831.5 num_examples: 6964 download_size: 895892605 dataset_size: 896860831.5 --- # Dataset Card for "audio-diffusion-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

KnutJaegersberg/Deita-6k

--- license: mit --- English subset of the data.

rai-sandeep/dataset_full_v3

--- dataset_info: features: - name: doctype dtype: string - name: section dtype: string - name: topic dtype: string - name: content dtype: string splits: - name: train num_bytes: 27897 num_examples: 26 download_size: 19998 dataset_size: 27897 --- # Dataset Card for "dataset_full_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jamespratama/tutorial-platypus-llamma

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4251526 num_examples: 1000 download_size: 2253085 dataset_size: 4251526 configs: - config_name: default data_files: - split: train path: data/train-* ---

lilacai/lilac-HellaSwag

--- tags: - Lilac --- # lilac/HellaSwag This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/Rowan/hellaswag](https://huggingface.co/datasets/Rowan/hellaswag) To download the dataset to a local directory: ```bash lilac download lilacai/lilac-HellaSwag ``` or from python with: ```py ll.download("lilacai/lilac-HellaSwag") ```

Elfsong/patient_info

--- configs: - config_name: default data_files: - split: anxiety path: data/anxiety-* - split: depression path: data/depression-* - split: ptsd path: data/ptsd-* - split: bipolar path: data/bipolar-* - split: substance_misuse path: data/substance_misuse-* - split: eating_disorders path: data/eating_disorders-* - split: alcohol_consumption path: data/alcohol_consumption-* dataset_info: features: - name: url dtype: string - name: comments list: - name: author_from sequence: string - name: author_to sequence: string - name: comments list: - name: author_from sequence: string - name: author_to sequence: string - name: content sequence: string - name: date sequence: string - name: content sequence: string - name: date sequence: string - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: content dtype: string - name: author dtype: string splits: - name: anxiety num_bytes: 143006120 num_examples: 27393 - name: depression num_bytes: 49953142 num_examples: 6982 - name: ptsd num_bytes: 1626957 num_examples: 349 - name: bipolar num_bytes: 3087512 num_examples: 474 - name: substance_misuse num_bytes: 1406369 num_examples: 195 - name: eating_disorders num_bytes: 1294592 num_examples: 233 - name: alcohol_consumption num_bytes: 21540333 num_examples: 1855 download_size: 109169290 dataset_size: 221915025 --- # Dataset Card for "patient_info" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sid-th26/prelims_all_questions

--- dataset_info: features: - name: section_name dtype: string - name: sub_section_name dtype: string - name: topic_name dtype: string - name: Question dtype: string - name: Option_A dtype: string - name: Option_B dtype: string - name: Option_C dtype: string - name: Option_D dtype: string - name: explanation dtype: string - name: difficulty dtype: string - name: answer dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 229012652 num_examples: 92171 download_size: 109175296 dataset_size: 229012652 configs: - config_name: default data_files: - split: train path: data/train-* ---

nuprl/MultiPL-E-synthetic-solutions

--- dataset_info: features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: solution dtype: string splits: - name: train num_bytes: 2185285 num_examples: 2624 download_size: 891673 dataset_size: 2185285 license: openrail language: - en pretty_name: MultiPL-E Synthetic Solutions --- # Dataset Card This is a dataset of partial solutions to the HumanEval and MBPP code generation benchmarks tranlated into 18+ programming languages. The original benchmark problems were in Python, and we build the dataset as follows: 1. We translate the prompts into a new language using MultiPL-E; 2. We use code-davinci-002 to generate 200 completions for each problem at temperature 0.8; 3. We select a working solution (if one exists) for each problem-language pair. [This notebook](https://github.com/nuprl/MultiPL-E/blob/main/notebooks/build_synthetic_solutions_dataset.ipynb) carried out the steps described above. Note that the dataset does *not* have solutions for every problem-language pair, since code-davinci-002 cannot produce a correct solution to every problem.

Joedwoo/NTT_version_1.0

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 3595 num_examples: 13 download_size: 4937 dataset_size: 3595 configs: - config_name: default data_files: - split: train path: data/train-* ---

cmarvolo/auto_fl

--- license: apache-2.0 task_categories: - text-generation language: - en tags: - not-for-all-audiences size_categories: - n<1K ---

insanemyrr/test-diploma-lucchi-cropped-new-mix-biggest

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': testing '1': training splits: - name: train num_bytes: 299779032.96 num_examples: 3960 - name: test num_bytes: 299751233.76 num_examples: 3960 download_size: 599433953 dataset_size: 599530266.72 --- # Dataset Card for "test-diploma-lucchi-cropped-new-mix-biggest" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-28000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 664600 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

polinaeterna/push_to_hub_many_configs

--- builder_configs: - config_name: custom data_files: - split: train pattern: custom/train-* - split: random pattern: custom/random-* - config_name: default data_files: - split: train pattern: data/train-* - split: random pattern: data/random-* dataset_info: - config_name: custom features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 1600 num_examples: 100 - name: random num_bytes: 160 num_examples: 10 download_size: 3650 dataset_size: 1760 - config_name: default features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 1600 num_examples: 100 - name: random num_bytes: 800 num_examples: 50 download_size: 4042 dataset_size: 2400 --- # Dataset Card for "push_to_hub_many_configs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/oosaki_amana_theidolmstershinycolors

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of oosaki_amana/大崎甘奈 (THE iDOLM@STER: SHINY COLORS) This is the dataset of oosaki_amana/大崎甘奈 (THE iDOLM@STER: SHINY COLORS), containing 500 images and their tags. The core tags of this character are `long_hair, bangs, brown_hair, yellow_eyes, breasts, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 978.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 475.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1311 | 1.04 GiB | [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 815.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1311 | 1.66 GiB | [Download](https://huggingface.co/datasets/CyberHarem/oosaki_amana_theidolmstershinycolors/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/oosaki_amana_theidolmstershinycolors', 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 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blush, cleavage, collarbone, looking_at_viewer, solo, bare_shoulders, necklace, long_sleeves, off-shoulder_sweater, sitting, closed_mouth, double_bun, dress, earrings, smile, swept_bangs | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, solo, blush, smile, collarbone, white_background, closed_mouth, simple_background, upper_body, bare_shoulders, brown_eyes, cleavage, choker, heart, sleeveless, white_dress | | 2 | 12 | ![](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, looking_at_viewer, necktie, plaid_skirt, school_uniform, solo, pleated_skirt, blush, open_mouth, :d, long_sleeves, white_shirt, braid, simple_background, sweater, blazer, collared_shirt, outdoors, petals, red_hair, white_background | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, collarbone, eyewear_on_head, heart-shaped_eyewear, navel, red_bikini, sunglasses, black_choker, floral_print, looking_at_viewer, simple_background, solo, swept_bangs, blush, bracelet, cleavage, earrings, necklace, one_eye_closed, open_mouth, white_background, :d, bare_shoulders, groin, thighs | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, gloves, looking_at_viewer, solo, white_coat, braid, open_mouth, smile, fur_hat, long_sleeves, snowing, white_headwear, winter_clothes, fur_trim, hat_bow, upper_body | | 5 | 5 | ![](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, blue_sky, blush, day, looking_at_viewer, navel, outdoors, solo, armpits, arms_up, cloud, midriff, arms_behind_head, cowboy_shot, hairband, :d, bikini_under_clothes, blue_shorts, brown_eyes, collarbone, denim_shorts, frills, hair_bow, open_mouth, ribbon, short_shorts, swept_bangs, tied_shirt | | 6 | 10 | ![](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, blush, looking_at_viewer, hair_ornament, obi, print_kimono, solo, floral_print, side_ponytail, sidelocks, hair_between_eyes, open_mouth, pink_kimono, wide_sleeves, :d, long_sleeves, yukata, blurry, swept_bangs, upper_body | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, blush, collarbone, looking_at_viewer, race_queen, solo, thigh_boots, thighhighs, black_choker, cleavage, hair_ribbon, holding_umbrella, navel, red_hair, belt, black_skirt, high_ponytail, miniskirt, open_mouth, smile, standing, swept_bangs, wrist_cuffs, bare_shoulders, black_footwear, full_body, ground_vehicle, hoop_earrings, midriff, mismatched_footwear, mismatched_legwear, motor_vehicle, nail_polish, official_alternate_costume, outdoors, sidelocks, sleeveless, tube_top | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | cleavage | collarbone | looking_at_viewer | solo | bare_shoulders | necklace | long_sleeves | off-shoulder_sweater | sitting | closed_mouth | double_bun | dress | earrings | smile | swept_bangs | white_background | simple_background | upper_body | brown_eyes | choker | heart | sleeveless | white_dress | necktie | plaid_skirt | school_uniform | pleated_skirt | open_mouth | :d | white_shirt | braid | sweater | blazer | collared_shirt | outdoors | petals | red_hair | eyewear_on_head | heart-shaped_eyewear | navel | red_bikini | sunglasses | black_choker | floral_print | bracelet | one_eye_closed | groin | thighs | gloves | white_coat | fur_hat | snowing | white_headwear | winter_clothes | fur_trim | hat_bow | blue_sky | day | armpits | arms_up | cloud | midriff | arms_behind_head | cowboy_shot | hairband | bikini_under_clothes | blue_shorts | denim_shorts | frills | hair_bow | ribbon | short_shorts | tied_shirt | hair_ornament | obi | print_kimono | side_ponytail | sidelocks | hair_between_eyes | pink_kimono | wide_sleeves | yukata | blurry | race_queen | thigh_boots | thighhighs | hair_ribbon | holding_umbrella | belt | black_skirt | high_ponytail | miniskirt | standing | wrist_cuffs | black_footwear | full_body | ground_vehicle | hoop_earrings | mismatched_footwear | mismatched_legwear | motor_vehicle | nail_polish | official_alternate_costume | tube_top | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------|:-------------|:--------------------|:-------|:-----------------|:-----------|:---------------|:-----------------------|:----------|:---------------|:-------------|:--------|:-----------|:--------|:--------------|:-------------------|:--------------------|:-------------|:-------------|:---------|:--------|:-------------|:--------------|:----------|:--------------|:-----------------|:----------------|:-------------|:-----|:--------------|:--------|:----------|:---------|:-----------------|:-----------|:---------|:-----------|:------------------|:-----------------------|:--------|:-------------|:-------------|:---------------|:---------------|:-----------|:-----------------|:--------|:---------|:---------|:-------------|:----------|:----------|:-----------------|:-----------------|:-----------|:----------|:-----------|:------|:----------|:----------|:--------|:----------|:-------------------|:--------------|:-----------|:-----------------------|:--------------|:---------------|:---------|:-----------|:---------|:---------------|:-------------|:----------------|:------|:---------------|:----------------|:------------|:--------------------|:--------------|:---------------|:---------|:---------|:-------------|:--------------|:-------------|:--------------|:-------------------|:-------|:--------------|:----------------|:------------|:-----------|:--------------|:-----------------|:------------|:-----------------|:----------------|:----------------------|:---------------------|:----------------|:--------------|:-----------------------------|:-----------| | 0 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 14 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | X | X | X | | | | | | | X | | X | X | X | | | | | | | | | | | X | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | X | X | X | | | | | | | | | | | X | | | | X | | | | | | | | | X | X | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 10 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | | | X | X | | | X | | | | | | | | X | | | X | | | | | | | | | | X | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | X | X | X | X | | | | | | | | | X | X | | | | | | | X | | | | | | X | | | | | | | X | | X | | | X | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

polinaeterna/old_push

--- dataset_info: features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 800 num_examples: 50 download_size: 1763 dataset_size: 800 --- # Dataset Card for "old_push" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-lener_br-lener_br-2a71c5-1777061680

--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: pierreguillou/ner-bert-base-cased-pt-lenerbr metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: validation col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: pierreguillou/ner-bert-base-cased-pt-lenerbr * Dataset: lener_br * Config: lener_br * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.

Orange/paraqa-sparqltotext

--- dataset_info: features: - name: uid dtype: string - name: query dtype: string - name: question dtype: string - name: simplified_query dtype: string - name: answer dtype: string - name: verbalized_answer dtype: string - name: verbalized_answer_2 dtype: string - name: verbalized_answer_3 dtype: string - name: verbalized_answer_4 dtype: string - name: verbalized_answer_5 dtype: string - name: verbalized_answer_6 dtype: string - name: verbalized_answer_7 dtype: string - name: verbalized_answer_8 dtype: string splits: - name: train num_bytes: 2540548 num_examples: 3500 - name: validation num_bytes: 369571 num_examples: 500 - name: test num_bytes: 722302 num_examples: 1000 download_size: 1750172 dataset_size: 3632421 task_categories: - conversational - question-answering - text-generation - text2text-generation tags: - qa - knowledge-graph - sparql --- # Dataset Card for ParaQA-SPARQLtoText ## Table of Contents - [Dataset Card for ParaQA-SPARQLtoText](#dataset-card-for-paraqa-sparqltotext) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [New field `simplified_query`](#new-field-simplified_query) - [New split "valid"](#new-split-valid) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Types of questions](#types-of-questions) - [Data splits](#data-splits) - [Additional information](#additional-information) - [Related datasets](#related-datasets) - [Licencing information](#licencing-information) - [Citation information](#citation-information) - [This version of the corpus (with normalized SPARQL queries)](#this-version-of-the-corpus-with-normalized-sparql-queries) - [Original version](#original-version) ## Dataset Description - **Paper:** [SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (AACL-IJCNLP 2022)](https://aclanthology.org/2022.aacl-main.11/) - **Point of Contact:** Gwénolé Lecorvé ### Dataset Summary Special version of ParaQA with SPARQL queries formatted for the SPARQL-to-Text task #### New field `simplified_query` New field is named "simplified_query". It results from applying the following step on the field "query": * Replacing URIs with a simpler format with prefix "resource:", "property:" and "ontology:". * Spacing the delimiters `(`, `{`, `.`, `}`, `)`. * Randomizing the variables names * Shuffling the clauses #### New split "valid" A validation set was randonly extracted from the test set to represent 10% of the whole dataset. ### Languages - English ## Dataset Structure ### Types of questions Comparison of question types compared to related datasets: | | | [SimpleQuestions](https://huggingface.co/datasets/OrangeInnov/simplequestions-sparqltotext) | [ParaQA](https://huggingface.co/datasets/OrangeInnov/paraqa-sparqltotext) | [LC-QuAD 2.0](https://huggingface.co/datasets/OrangeInnov/lcquad_2.0-sparqltotext) | [CSQA](https://huggingface.co/datasets/OrangeInnov/csqa-sparqltotext) | [WebNLQ-QA](https://huggingface.co/datasets/OrangeInnov/webnlg-qa) | |--------------------------|-----------------|:---------------:|:------:|:-----------:|:----:|:---------:| | **Number of triplets in query** | 1 | ✓ | ✓ | ✓ | ✓ | ✓ | | | 2 | | ✓ | ✓ | ✓ | ✓ | | | More | | | ✓ | ✓ | ✓ | | **Logical connector between triplets** | Conjunction | ✓ | ✓ | ✓ | ✓ | ✓ | | | Disjunction | | | | ✓ | ✓ | | | Exclusion | | | | ✓ | ✓ | | **Topology of the query graph** | Direct | ✓ | ✓ | ✓ | ✓ | ✓ | | | Sibling | | ✓ | ✓ | ✓ | ✓ | | | Chain | | ✓ | ✓ | ✓ | ✓ | | | Mixed | | | ✓ | | ✓ | | | Other | | ✓ | ✓ | ✓ | ✓ | | **Variable typing in the query** | None | ✓ | ✓ | ✓ | ✓ | ✓ | | | Target variable | | ✓ | ✓ | ✓ | ✓ | | | Internal variable | | ✓ | ✓ | ✓ | ✓ | | **Comparisons clauses** | None | ✓ | ✓ | ✓ | ✓ | ✓ | | | String | | | ✓ | | ✓ | | | Number | | | ✓ | ✓ | ✓ | | | Date | | | ✓ | | ✓ | | **Superlative clauses** | No | ✓ | ✓ | ✓ | ✓ | ✓ | | | Yes | | | | ✓ | | | **Answer type** | Entity (open) | ✓ | ✓ | ✓ | ✓ | ✓ | | | Entity (closed) | | | | ✓ | ✓ | | | Number | | | ✓ | ✓ | ✓ | | | Boolean | | ✓ | ✓ | ✓ | ✓ | | **Answer cardinality** | 0 (unanswerable) | | | ✓ | | ✓ | | | 1 | ✓ | ✓ | ✓ | ✓ | ✓ | | | More | | ✓ | ✓ | ✓ | ✓ | | **Number of target variables** | 0 (⇒ ASK verb) | | ✓ | ✓ | ✓ | ✓ | | | 1 | ✓ | ✓ | ✓ | ✓ | ✓ | | | 2 | | | ✓ | | ✓ | | **Dialogue context** | Self-sufficient | ✓ | ✓ | ✓ | ✓ | ✓ | | | Coreference | | | | ✓ | ✓ | | | Ellipsis | | | | ✓ | ✓ | | **Meaning** | Meaningful | ✓ | ✓ | ✓ | ✓ | ✓ | | | Non-sense | | | | | ✓ | ### Data splits Text verbalization is only available for a subset of the test set, referred to as *challenge set*. Other sample only contain dialogues in the form of follow-up sparql queries. | | Train | Validation | Test | | --------------------- | ---------- | ---------- | ---------- | | Questions | 3,500 | 500 | 1,000 | | NL question per query | 1 | | Characters per query | 103 (± 27) | | Tokens per question | 10.3 (± 3.7) | ## Additional information ### Related datasets This corpus is part of a set of 5 datasets released for SPARQL-to-Text generation, namely: - Non conversational datasets - [SimpleQuestions](https://huggingface.co/datasets/OrangeInnov/simplequestions-sparqltotext) (from https://github.com/askplatypus/wikidata-simplequestions) - [ParaQA](https://huggingface.co/datasets/OrangeInnov/paraqa-sparqltotext) (from https://github.com/barshana-banerjee/ParaQA) - [LC-QuAD 2.0](https://huggingface.co/datasets/OrangeInnov/lcquad_2.0-sparqltotext) (from http://lc-quad.sda.tech/) - Conversational datasets - [CSQA](https://huggingface.co/datasets/OrangeInnov/csqa-sparqltotext) (from https://amritasaha1812.github.io/CSQA/) - [WebNLQ-QA](https://huggingface.co/datasets/OrangeInnov/webnlg-qa) (derived from https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/release_v3.0) ### Licencing information * Content from original dataset: CC-BY 4.0 * New content: CC BY-SA 4.0 ### Citation information #### This version of the corpus (with normalized SPARQL queries) ```bibtex @inproceedings{lecorve2022sparql2text, title={SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications}, author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.}, journal={Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)}, year={2022} } ``` #### Original version ```bibtex @inproceedings{kacupaj2021paraqa, title={Paraqa: a question answering dataset with paraphrase responses for single-turn conversation}, author={Kacupaj, Endri and Banerjee, Barshana and Singh, Kuldeep and Lehmann, Jens}, booktitle={European semantic web conference}, pages={598--613}, year={2021}, organization={Springer} } ```

CyberHarem/sumi_otokawa_sakuratrick

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Sumi Otokawa This is the dataset of Sumi Otokawa, containing 49 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------| | raw | 49 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 117 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 145 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 49 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 49 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 49 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 117 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 117 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 101 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 145 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 145 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

open-llm-leaderboard/details_cognitivecomputations__MegaDolphin-120b

--- pretty_name: Evaluation run of cognitivecomputations/MegaDolphin-120b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cognitivecomputations/MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b)\ \ 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__MegaDolphin-120b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-21T21:48:58.549252](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__MegaDolphin-120b/blob/main/results_2024-01-21T21-48-58.549252.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\": 0.6902282983987049,\n\ \ \"acc_stderr\": 0.03061680900457877,\n \"acc_norm\": 0.6956826713561578,\n\ \ \"acc_norm_stderr\": 0.03120808276540501,\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.017240861812099804,\n \"mc2\": 0.592821117756168,\n\ \ \"mc2_stderr\": 0.015249093012153285\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620196,\n\ \ \"acc_norm\": 0.6902730375426621,\n \"acc_norm_stderr\": 0.013512058415238361\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7026488747261501,\n\ \ \"acc_stderr\": 0.004561582009834577,\n \"acc_norm\": 0.8780123481378211,\n\ \ \"acc_norm_stderr\": 0.0032660269509226444\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\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7828947368421053,\n \"acc_stderr\": 0.03355045304882923,\n\ \ \"acc_norm\": 0.7828947368421053,\n \"acc_norm_stderr\": 0.03355045304882923\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\ \ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \ \ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.0271342916287417,\n\ \ \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.0271342916287417\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\ \ \"acc_stderr\": 0.03216600808802268,\n \"acc_norm\": 0.8194444444444444,\n\ \ \"acc_norm_stderr\": 0.03216600808802268\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\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\": 0.6936416184971098,\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\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062946,\n\ \ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062946\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6893617021276596,\n \"acc_stderr\": 0.03025123757921317,\n\ \ \"acc_norm\": 0.6893617021276596,\n \"acc_norm_stderr\": 0.03025123757921317\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6620689655172414,\n \"acc_stderr\": 0.039417076320648906,\n\ \ \"acc_norm\": 0.6620689655172414,\n \"acc_norm_stderr\": 0.039417076320648906\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.47619047619047616,\n \"acc_stderr\": 0.025722097064388525,\n \"\ acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.025722097064388525\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8290322580645161,\n \"acc_stderr\": 0.02141724293632158,\n \"\ acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.02141724293632158\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\"\ : 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781678,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781678\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8585858585858586,\n \"acc_stderr\": 0.02482590979334335,\n \"\ acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.02482590979334335\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7461538461538462,\n \"acc_stderr\": 0.022066054378726257,\n\ \ \"acc_norm\": 0.7461538461538462,\n \"acc_norm_stderr\": 0.022066054378726257\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606647,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606647\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295894,\n \ \ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295894\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4370860927152318,\n \"acc_stderr\": 0.04050035722230636,\n \"\ acc_norm\": 0.4370860927152318,\n \"acc_norm_stderr\": 0.04050035722230636\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8844036697247707,\n \"acc_stderr\": 0.01370874953417264,\n \"\ acc_norm\": 0.8844036697247707,\n \"acc_norm_stderr\": 0.01370874953417264\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"\ acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073315,\n \"\ acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073315\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.890295358649789,\n \"acc_stderr\": 0.020343400734868834,\n \ \ \"acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.020343400734868834\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7847533632286996,\n\ \ \"acc_stderr\": 0.02758406660220827,\n \"acc_norm\": 0.7847533632286996,\n\ \ \"acc_norm_stderr\": 0.02758406660220827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622814,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622814\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\": 0.8466257668711656,\n \"acc_stderr\": 0.028311601441438607,\n\ \ \"acc_norm\": 0.8466257668711656,\n \"acc_norm_stderr\": 0.028311601441438607\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5714285714285714,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.5714285714285714,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822582,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822582\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n\ \ \"acc_stderr\": 0.019119892798924974,\n \"acc_norm\": 0.905982905982906,\n\ \ \"acc_norm_stderr\": 0.019119892798924974\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8441890166028098,\n\ \ \"acc_stderr\": 0.012969269247762578,\n \"acc_norm\": 0.8441890166028098,\n\ \ \"acc_norm_stderr\": 0.012969269247762578\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7832369942196532,\n \"acc_stderr\": 0.022183477668412856,\n\ \ \"acc_norm\": 0.7832369942196532,\n \"acc_norm_stderr\": 0.022183477668412856\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5150837988826815,\n\ \ \"acc_stderr\": 0.01671489037999606,\n \"acc_norm\": 0.5150837988826815,\n\ \ \"acc_norm_stderr\": 0.01671489037999606\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7684887459807074,\n\ \ \"acc_stderr\": 0.023956532766639133,\n \"acc_norm\": 0.7684887459807074,\n\ \ \"acc_norm_stderr\": 0.023956532766639133\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.02161380939522479,\n\ \ \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.02161380939522479\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5567375886524822,\n \"acc_stderr\": 0.029634838473766006,\n \ \ \"acc_norm\": 0.5567375886524822,\n \"acc_norm_stderr\": 0.029634838473766006\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5867014341590613,\n\ \ \"acc_stderr\": 0.012576779494860076,\n \"acc_norm\": 0.5867014341590613,\n\ \ \"acc_norm_stderr\": 0.012576779494860076\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7095588235294118,\n \"acc_stderr\": 0.027576468622740533,\n\ \ \"acc_norm\": 0.7095588235294118,\n \"acc_norm_stderr\": 0.027576468622740533\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7565359477124183,\n \"acc_stderr\": 0.017362473762146606,\n \ \ \"acc_norm\": 0.7565359477124183,\n \"acc_norm_stderr\": 0.017362473762146606\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7714285714285715,\n \"acc_stderr\": 0.02688214492230774,\n\ \ \"acc_norm\": 0.7714285714285715,\n \"acc_norm_stderr\": 0.02688214492230774\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306046,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306046\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\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\": 0.027966785859160896,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160896\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.017240861812099804,\n \"mc2\": 0.592821117756168,\n\ \ \"mc2_stderr\": 0.015249093012153285\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8184688239936859,\n \"acc_stderr\": 0.010833276515007508\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4624715693707354,\n \ \ \"acc_stderr\": 0.013733636059107756\n }\n}\n```" repo_url: https://huggingface.co/cognitivecomputations/MegaDolphin-120b 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_21T21_48_58.549252 path: - '**/details_harness|arc:challenge|25_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-21T21-48-58.549252.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|gsm8k|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hellaswag|10_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-21T21-48-58.549252.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-management|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T21-48-58.549252.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|truthfulqa:mc|0_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-21T21-48-58.549252.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_21T21_48_58.549252 path: - '**/details_harness|winogrande|5_2024-01-21T21-48-58.549252.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-21T21-48-58.549252.parquet' - config_name: results data_files: - split: 2024_01_21T21_48_58.549252 path: - results_2024-01-21T21-48-58.549252.parquet - split: latest path: - results_2024-01-21T21-48-58.549252.parquet --- # Dataset Card for Evaluation run of cognitivecomputations/MegaDolphin-120b  Dataset automatically created during the evaluation run of model [cognitivecomputations/MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b) 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__MegaDolphin-120b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-21T21:48:58.549252](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__MegaDolphin-120b/blob/main/results_2024-01-21T21-48-58.549252.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": 0.6902282983987049, "acc_stderr": 0.03061680900457877, "acc_norm": 0.6956826713561578, "acc_norm_stderr": 0.03120808276540501, "mc1": 0.41370869033047736, "mc1_stderr": 0.017240861812099804, "mc2": 0.592821117756168, "mc2_stderr": 0.015249093012153285 }, "harness|arc:challenge|25": { "acc": 0.643344709897611, "acc_stderr": 0.013998056902620196, "acc_norm": 0.6902730375426621, "acc_norm_stderr": 0.013512058415238361 }, "harness|hellaswag|10": { "acc": 0.7026488747261501, "acc_stderr": 0.004561582009834577, "acc_norm": 0.8780123481378211, "acc_norm_stderr": 0.0032660269509226444 }, "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": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7828947368421053, "acc_stderr": 0.03355045304882923, "acc_norm": 0.7828947368421053, "acc_norm_stderr": 0.03355045304882923 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7358490566037735, "acc_stderr": 0.0271342916287417, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.0271342916287417 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8194444444444444, "acc_stderr": 0.03216600808802268, "acc_norm": 0.8194444444444444, "acc_norm_stderr": 0.03216600808802268 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "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": 0.6936416184971098, "acc_stderr": 0.035149425512674394, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.035149425512674394 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.35294117647058826, "acc_stderr": 0.04755129616062946, "acc_norm": 0.35294117647058826, "acc_norm_stderr": 0.04755129616062946 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816507, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816507 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6893617021276596, "acc_stderr": 0.03025123757921317, "acc_norm": 0.6893617021276596, "acc_norm_stderr": 0.03025123757921317 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6620689655172414, "acc_stderr": 0.039417076320648906, "acc_norm": 0.6620689655172414, "acc_norm_stderr": 0.039417076320648906 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.47619047619047616, "acc_stderr": 0.025722097064388525, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.025722097064388525 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5238095238095238, "acc_stderr": 0.04467062628403273, "acc_norm": 0.5238095238095238, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8290322580645161, "acc_stderr": 0.02141724293632158, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 0.02141724293632158 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.03517945038691063, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781678, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781678 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8585858585858586, "acc_stderr": 0.02482590979334335, "acc_norm": 0.8585858585858586, "acc_norm_stderr": 0.02482590979334335 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7461538461538462, "acc_stderr": 0.022066054378726257, "acc_norm": 0.7461538461538462, "acc_norm_stderr": 0.022066054378726257 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.02831753349606647, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.02831753349606647 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7815126050420168, "acc_stderr": 0.02684151432295894, "acc_norm": 0.7815126050420168, "acc_norm_stderr": 0.02684151432295894 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4370860927152318, "acc_stderr": 0.04050035722230636, "acc_norm": 0.4370860927152318, "acc_norm_stderr": 0.04050035722230636 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8844036697247707, "acc_stderr": 0.01370874953417264, "acc_norm": 0.8844036697247707, "acc_norm_stderr": 0.01370874953417264 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5787037037037037, "acc_stderr": 0.03367462138896078, "acc_norm": 0.5787037037037037, "acc_norm_stderr": 0.03367462138896078 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9313725490196079, "acc_stderr": 0.017744453647073315, "acc_norm": 0.9313725490196079, "acc_norm_stderr": 0.017744453647073315 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.890295358649789, "acc_stderr": 0.020343400734868834, "acc_norm": 0.890295358649789, "acc_norm_stderr": 0.020343400734868834 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7847533632286996, "acc_stderr": 0.02758406660220827, "acc_norm": 0.7847533632286996, "acc_norm_stderr": 0.02758406660220827 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494733, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494733 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622814, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622814 }, "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": 0.8466257668711656, "acc_stderr": 0.028311601441438607, "acc_norm": 0.8466257668711656, "acc_norm_stderr": 0.028311601441438607 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5714285714285714, "acc_stderr": 0.04697113923010212, "acc_norm": 0.5714285714285714, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822582, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822582 }, "harness|hendrycksTest-marketing|5": { "acc": 0.905982905982906, "acc_stderr": 0.019119892798924974, "acc_norm": 0.905982905982906, "acc_norm_stderr": 0.019119892798924974 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.67, "acc_stderr": 0.04725815626252606, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8441890166028098, "acc_stderr": 0.012969269247762578, "acc_norm": 0.8441890166028098, "acc_norm_stderr": 0.012969269247762578 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7832369942196532, "acc_stderr": 0.022183477668412856, "acc_norm": 0.7832369942196532, "acc_norm_stderr": 0.022183477668412856 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5150837988826815, "acc_stderr": 0.01671489037999606, "acc_norm": 0.5150837988826815, "acc_norm_stderr": 0.01671489037999606 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7684887459807074, "acc_stderr": 0.023956532766639133, "acc_norm": 0.7684887459807074, "acc_norm_stderr": 0.023956532766639133 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8148148148148148, "acc_stderr": 0.02161380939522479, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.02161380939522479 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5567375886524822, "acc_stderr": 0.029634838473766006, "acc_norm": 0.5567375886524822, "acc_norm_stderr": 0.029634838473766006 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5867014341590613, "acc_stderr": 0.012576779494860076, "acc_norm": 0.5867014341590613, "acc_norm_stderr": 0.012576779494860076 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7095588235294118, "acc_stderr": 0.027576468622740533, "acc_norm": 0.7095588235294118, "acc_norm_stderr": 0.027576468622740533 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7565359477124183, "acc_stderr": 0.017362473762146606, "acc_norm": 0.7565359477124183, "acc_norm_stderr": 0.017362473762146606 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7714285714285715, "acc_stderr": 0.02688214492230774, "acc_norm": 0.7714285714285715, "acc_norm_stderr": 0.02688214492230774 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306046, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306046 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "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": 0.027966785859160896, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160896 }, "harness|truthfulqa:mc|0": { "mc1": 0.41370869033047736, "mc1_stderr": 0.017240861812099804, "mc2": 0.592821117756168, "mc2_stderr": 0.015249093012153285 }, "harness|winogrande|5": { "acc": 0.8184688239936859, "acc_stderr": 0.010833276515007508 }, "harness|gsm8k|5": { "acc": 0.4624715693707354, "acc_stderr": 0.013733636059107756 } } ``` ## 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]

PanoEvJ/GenAI-sample

--- dataset_info: features: - name: name dtype: string - name: description dtype: string - name: ad dtype: string splits: - name: train num_bytes: 3211 num_examples: 5 download_size: 0 dataset_size: 3211 --- # Dataset Card for "GenAI-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lalanikarim/glaive-function-calling-v2

--- language: - en license: apache-2.0 dataset_info: features: - name: chat dtype: string - name: system dtype: string - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 157115139.42965484 num_examples: 71678 - name: test num_bytes: 17458942.570345167 num_examples: 7965 download_size: 47435441 dataset_size: 174574082.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

claudiostu/voz1

--- license: openrail ---

ControlNet/LAV-DF

--- language: - en tags: - deepfakes - video pretty_name: LAV-DF task_categories: - video-classification paperswithcode_id: lav-df license: cc size_categories: - 100K<n<1M extra_gated_heading: Access LAV-DF dataset on Hugging Face extra_gated_prompt: To use this LAV-DF dataset, you should agree the [terms and conditions](https://github.com/ControlNet/LAV-DF/blob/master/TERMS_AND_CONDITIONS.md). --- # Localized Audio Visual DeepFake Dataset (LAV-DF) This repo is the dataset for the DICTA paper [Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization](https://ieeexplore.ieee.org/document/10034605) (Best Award), and the journal paper ["Glitch in the Matrix!": A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization](https://arxiv.org/abs/2305.01979) submitted to CVIU. ## LAV-DF Dataset ### Download To use this LAV-DF dataset, you should agree the [terms and conditions](https://github.com/ControlNet/LAV-DF/blob/master/TERMS_AND_CONDITIONS.md). Download link: [Google Drive](https://drive.google.com/file/d/1-OQ-NDtdEyqHNLaZU1Lt9Upk5wVqfYJw/view?usp=sharing), or [HuggingFace](https://huggingface.co/datasets/ControlNet/LAV-DF). ## License This project is under the CC BY-NC 4.0 license. See [LICENSE](https://github.com/ControlNet/LAV-DF/blob/master/LICENSE) for details. ## References If you find this work useful in your research, please cite them. The conference paper, ```bibtex @inproceedings{cai2022you, title = {Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization}, author = {Cai, Zhixi and Stefanov, Kalin and Dhall, Abhinav and Hayat, Munawar}, booktitle = {2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)}, year = {2022}, doi = {10.1109/DICTA56598.2022.10034605}, pages = {1--10}, address = {Sydney, Australia}, } ``` The extended journal version currently under review at CVIU, ```bibtex @article{cai2023glitch, title = {Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization}, author = {Cai, Zhixi and Ghosh, Shreya and Dhall, Abhinav and Gedeon, Tom and Stefanov, Kalin and Hayat, Munawar}, journal = {arXiv preprint arXiv:2305.01979}, year = {2023}, } ```

HiTZ/casimedicos-squad

--- license: cc-by-4.0 language: - es tags: - casimedicos - explainability - medical exams - medical question answering - extractive question answering - squad - multilinguality - LLMs - LLM pretty_name: casimedicos-squad configs: - config_name: es data_files: - split: train path: - data/es/es_train_casimedicos_squad.json - split: validation path: - data/es/es_dev_casimedicos_squad.json - split: test path: - data/es/es_test_casimedicos_squad.json task_categories: - question-answering size_categories: - 1K<n<10K --- <p align="center"> <br> <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 200px;"> <br> # Antidote CasiMedicos in SQuAD Format for Explanatory Argument Extraction We present a new multilingual parallel medical dataset of commented medical exams which includes not only explanatory arguments for the correct answer but also arguments to explain why the remaining possible answers are incorrect. Furthermore, this dataset allows us to setup a **novel extractive task** which consists of **identifying the explanation of the correct answer written by medical doctors**. In order to do so we leverage the SQuAD extractive QA paradigm to automatically evaluate performance of language models to identify the explanation of the correct answer in medical exams without relying on costly manual evaluation by medical experts. The data source consists of Resident Medical Intern or Médico Interno Residente (MIR) exams, originally created by [CasiMedicos](https://www.casimedicos.com), a Spanish community of medical professionals who collaboratively, voluntarily, and free of charge, publishes written explanations about the possible answers included in the MIR exams. The aim is to generate a resource that helps future medical doctors to study towards the MIR examinations. The commented MIR exams, including the explanations, are published in the [CasiMedicos Project MIR 2.0 website](https://www.casimedicos.com/mir-2-0/). We have extracted, clean, structure and annotated the available data so that each document in **casimedicos-squad** includes the clinical case, the correct answer, the multiple-choice questions and the commented exam written by native Spanish medical doctors. The comments have been annotated with the span in the text that corresponds to the explanation of the correct answer (see example below). <table style="width:33%"> <tr> <th>casimedicos-squad splits</th> <tr> <td>train</td> <td>404</td> </tr> <tr> <td>validation</td> <td>56</td> </tr> <tr> <td>test</td> <td>119</td> </tr> </table> - 📖 Paper:[Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://arxiv.org/abs/2312.00567) - 💻 Github Repo (Data and Code): [https://github.com/ixa-ehu/antidote-casimedicos](https://github.com/ixa-ehu/antidote-casimedicos) - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote) - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR ## Example <p align="center"> <img src="https://github.com/ixa-ehu/antidote-casimedicos/blob/main/casimedicos-exp.png?raw=true" style="height: 650px;"> </p> The example above shows a document in CasiMedicos containing the textual content, including Clinical Case (C), Question (Q), Possible Answers (P), and Explanation (E). Furthermore, for **casimedicos-squad** we annotated the span in the explanation (E) that corresponds to the correct answer (A). The process of manually annotating the corpus consisted of specifying where the explanations of the correct answers begin and end. In order to obtain grammatically complete correct answer explanations, annotating full sentences or subordinate clauses was preferred over shorter spans. ## Data Explanation The dataset is structured as a list of documents ("paragraphs") where each of them include: - **context**: the explanation (E) in the document - **qas**: list of possible answers and questions. This element contains: - **answers**: an answer which corresponds to the explanation of the correct answer (A) - **question**: the clinical case (C) and question (Q) - **id**: unique identifier for the document ## Citation If you use this data please **cite the following paper**: ```bibtex @misc{goenaga2023explanatory, title={Explanatory Argument Extraction of Correct Answers in Resident Medical Exams}, author={Iakes Goenaga and Aitziber Atutxa and Koldo Gojenola and Maite Oronoz and Rodrigo Agerri}, year={2023}, eprint={2312.00567}, archivePrefix={arXiv} } ``` **Contact**: [Iakes Goenaga](http://www.hitz.eus/es/node/65) and [Rodrigo Agerri](https://ragerri.github.io/) HiTZ Center - Ixa, University of the Basque Country UPV/EHU

NobodyExistsOnTheInternet/ConvoOrcaShareGPT4096

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: evolutions sequence: string - name: textversion dtype: string - name: tokens dtype: int64 - name: too_long dtype: bool splits: - name: train num_bytes: 1808371367 num_examples: 46563 download_size: 882548173 dataset_size: 1808371367 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_jondurbin__bagel-34b-v0.2

--- pretty_name: Evaluation run of jondurbin/bagel-34b-v0.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jondurbin/bagel-34b-v0.2](https://huggingface.co/jondurbin/bagel-34b-v0.2) 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_jondurbin__bagel-34b-v0.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-05T02:46:07.466495](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__bagel-34b-v0.2/blob/main/results_2024-01-05T02-46-07.466495.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\": 0.7566584880323868,\n\ \ \"acc_stderr\": 0.028404446518444006,\n \"acc_norm\": 0.7644443889276894,\n\ \ \"acc_norm_stderr\": 0.028932547734181486,\n \"mc1\": 0.4369645042839657,\n\ \ \"mc1_stderr\": 0.017363844503195985,\n \"mc2\": 0.592598246243346,\n\ \ \"mc2_stderr\": 0.014870176336077599\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.64419795221843,\n \"acc_stderr\": 0.01399057113791876,\n\ \ \"acc_norm\": 0.6877133105802048,\n \"acc_norm_stderr\": 0.013542598541688065\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6347341167098187,\n\ \ \"acc_stderr\": 0.004805205798724566,\n \"acc_norm\": 0.8371838279227246,\n\ \ \"acc_norm_stderr\": 0.0036844333238877946\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.868421052631579,\n \"acc_stderr\": 0.027508689533549915,\n\ \ \"acc_norm\": 0.868421052631579,\n \"acc_norm_stderr\": 0.027508689533549915\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\": 0.8,\n \"acc_stderr\": 0.024618298195866514,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.024618298195866514\n \ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8958333333333334,\n\ \ \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n\ \ \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562429,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562429\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7514450867052023,\n\ \ \"acc_stderr\": 0.03295304696818317,\n \"acc_norm\": 0.7514450867052023,\n\ \ \"acc_norm_stderr\": 0.03295304696818317\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.049406356306056595\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\": 0.7914893617021277,\n \"acc_stderr\": 0.026556982117838742,\n\ \ \"acc_norm\": 0.7914893617021277,\n \"acc_norm_stderr\": 0.026556982117838742\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5614035087719298,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7655172413793103,\n \"acc_stderr\": 0.035306258743465914,\n\ \ \"acc_norm\": 0.7655172413793103,\n \"acc_norm_stderr\": 0.035306258743465914\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.7037037037037037,\n \"acc_stderr\": 0.023517294335963286,\n \"\ acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.023517294335963286\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.6031746031746031,\n\ \ \"acc_stderr\": 0.043758884927270585,\n \"acc_norm\": 0.6031746031746031,\n\ \ \"acc_norm_stderr\": 0.043758884927270585\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8903225806451613,\n\ \ \"acc_stderr\": 0.01777677870048519,\n \"acc_norm\": 0.8903225806451613,\n\ \ \"acc_norm_stderr\": 0.01777677870048519\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.645320197044335,\n \"acc_stderr\": 0.03366124489051449,\n\ \ \"acc_norm\": 0.645320197044335,\n \"acc_norm_stderr\": 0.03366124489051449\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\"\ : 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295138,\n\ \ \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295138\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8179487179487179,\n \"acc_stderr\": 0.019565236782930893,\n\ \ \"acc_norm\": 0.8179487179487179,\n \"acc_norm_stderr\": 0.019565236782930893\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.43333333333333335,\n \"acc_stderr\": 0.030213340289237924,\n \ \ \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.030213340289237924\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8403361344537815,\n \"acc_stderr\": 0.023793353997528802,\n\ \ \"acc_norm\": 0.8403361344537815,\n \"acc_norm_stderr\": 0.023793353997528802\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\"\ : 0.9155963302752294,\n \"acc_stderr\": 0.011918819327334872,\n \"\ acc_norm\": 0.9155963302752294,\n \"acc_norm_stderr\": 0.011918819327334872\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6620370370370371,\n \"acc_stderr\": 0.03225941352631295,\n \"\ acc_norm\": 0.6620370370370371,\n \"acc_norm_stderr\": 0.03225941352631295\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813905,\n \"\ acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813905\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9071729957805907,\n \"acc_stderr\": 0.01888975055095671,\n \ \ \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.01888975055095671\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8071748878923767,\n\ \ \"acc_stderr\": 0.026478240960489365,\n \"acc_norm\": 0.8071748878923767,\n\ \ \"acc_norm_stderr\": 0.026478240960489365\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.028718776889342327,\n\ \ \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.028718776889342327\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.0291998024556228,\n \"acc_norm\"\ : 0.8842975206611571,\n \"acc_norm_stderr\": 0.0291998024556228\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.030381596756651655,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.030381596756651655\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n\ \ \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5803571428571429,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.5803571428571429,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8932038834951457,\n \"acc_stderr\": 0.030581088928331356,\n\ \ \"acc_norm\": 0.8932038834951457,\n \"acc_norm_stderr\": 0.030581088928331356\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.015537514263253869,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.015537514263253869\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9067688378033205,\n\ \ \"acc_stderr\": 0.010397417087292847,\n \"acc_norm\": 0.9067688378033205,\n\ \ \"acc_norm_stderr\": 0.010397417087292847\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8265895953757225,\n \"acc_stderr\": 0.020383229551135022,\n\ \ \"acc_norm\": 0.8265895953757225,\n \"acc_norm_stderr\": 0.020383229551135022\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.8,\n\ \ \"acc_stderr\": 0.013378001241813075,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.013378001241813075\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8398692810457516,\n \"acc_stderr\": 0.020998740930362303,\n\ \ \"acc_norm\": 0.8398692810457516,\n \"acc_norm_stderr\": 0.020998740930362303\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8038585209003215,\n\ \ \"acc_stderr\": 0.022552447780478033,\n \"acc_norm\": 0.8038585209003215,\n\ \ \"acc_norm_stderr\": 0.022552447780478033\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8580246913580247,\n \"acc_stderr\": 0.019420260109438293,\n\ \ \"acc_norm\": 0.8580246913580247,\n \"acc_norm_stderr\": 0.019420260109438293\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6276595744680851,\n \"acc_stderr\": 0.02883892147125145,\n \ \ \"acc_norm\": 0.6276595744680851,\n \"acc_norm_stderr\": 0.02883892147125145\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5847457627118644,\n\ \ \"acc_stderr\": 0.012585471793400664,\n \"acc_norm\": 0.5847457627118644,\n\ \ \"acc_norm_stderr\": 0.012585471793400664\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8051470588235294,\n \"acc_stderr\": 0.02406059942348742,\n\ \ \"acc_norm\": 0.8051470588235294,\n \"acc_norm_stderr\": 0.02406059942348742\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8104575163398693,\n \"acc_stderr\": 0.015856152189980256,\n \ \ \"acc_norm\": 0.8104575163398693,\n \"acc_norm_stderr\": 0.015856152189980256\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\": 0.8326530612244898,\n \"acc_stderr\": 0.02389714476891452,\n\ \ \"acc_norm\": 0.8326530612244898,\n \"acc_norm_stderr\": 0.02389714476891452\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.9104477611940298,\n\ \ \"acc_stderr\": 0.02019067053502791,\n \"acc_norm\": 0.9104477611940298,\n\ \ \"acc_norm_stderr\": 0.02019067053502791\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072864,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072864\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4369645042839657,\n\ \ \"mc1_stderr\": 0.017363844503195985,\n \"mc2\": 0.592598246243346,\n\ \ \"mc2_stderr\": 0.014870176336077599\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8382004735595896,\n \"acc_stderr\": 0.010350128010292406\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.46171341925701287,\n \ \ \"acc_stderr\": 0.013732048227016682\n }\n}\n```" repo_url: https://huggingface.co/jondurbin/bagel-34b-v0.2 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_05T02_46_07.466495 path: - '**/details_harness|arc:challenge|25_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-05T02-46-07.466495.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|gsm8k|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hellaswag|10_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T02-46-07.466495.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T02-46-07.466495.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T02-46-07.466495.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_05T02_46_07.466495 path: - '**/details_harness|winogrande|5_2024-01-05T02-46-07.466495.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-05T02-46-07.466495.parquet' - config_name: results data_files: - split: 2024_01_05T02_46_07.466495 path: - results_2024-01-05T02-46-07.466495.parquet - split: latest path: - results_2024-01-05T02-46-07.466495.parquet --- # Dataset Card for Evaluation run of jondurbin/bagel-34b-v0.2  Dataset automatically created during the evaluation run of model [jondurbin/bagel-34b-v0.2](https://huggingface.co/jondurbin/bagel-34b-v0.2) 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_jondurbin__bagel-34b-v0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-05T02:46:07.466495](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__bagel-34b-v0.2/blob/main/results_2024-01-05T02-46-07.466495.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": 0.7566584880323868, "acc_stderr": 0.028404446518444006, "acc_norm": 0.7644443889276894, "acc_norm_stderr": 0.028932547734181486, "mc1": 0.4369645042839657, "mc1_stderr": 0.017363844503195985, "mc2": 0.592598246243346, "mc2_stderr": 0.014870176336077599 }, "harness|arc:challenge|25": { "acc": 0.64419795221843, "acc_stderr": 0.01399057113791876, "acc_norm": 0.6877133105802048, "acc_norm_stderr": 0.013542598541688065 }, "harness|hellaswag|10": { "acc": 0.6347341167098187, "acc_stderr": 0.004805205798724566, "acc_norm": 0.8371838279227246, "acc_norm_stderr": 0.0036844333238877946 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.868421052631579, "acc_stderr": 0.027508689533549915, "acc_norm": 0.868421052631579, "acc_norm_stderr": 0.027508689533549915 }, "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": 0.8, "acc_stderr": 0.024618298195866514, "acc_norm": 0.8, "acc_norm_stderr": 0.024618298195866514 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145633, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.43, "acc_stderr": 0.04975698519562429, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562429 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7514450867052023, "acc_stderr": 0.03295304696818317, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.03295304696818317 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5588235294117647, "acc_stderr": 0.049406356306056595, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.049406356306056595 }, "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": 0.7914893617021277, "acc_stderr": 0.026556982117838742, "acc_norm": 0.7914893617021277, "acc_norm_stderr": 0.026556982117838742 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5614035087719298, "acc_stderr": 0.04668000738510455, "acc_norm": 0.5614035087719298, "acc_norm_stderr": 0.04668000738510455 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7655172413793103, "acc_stderr": 0.035306258743465914, "acc_norm": 0.7655172413793103, "acc_norm_stderr": 0.035306258743465914 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.7037037037037037, "acc_stderr": 0.023517294335963286, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.023517294335963286 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.6031746031746031, "acc_stderr": 0.043758884927270585, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.043758884927270585 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8903225806451613, "acc_stderr": 0.01777677870048519, "acc_norm": 0.8903225806451613, "acc_norm_stderr": 0.01777677870048519 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.645320197044335, "acc_stderr": 0.03366124489051449, "acc_norm": 0.645320197044335, "acc_norm_stderr": 0.03366124489051449 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.8, "acc_stderr": 0.04020151261036845, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9637305699481865, "acc_stderr": 0.013492659751295138, "acc_norm": 0.9637305699481865, "acc_norm_stderr": 0.013492659751295138 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8179487179487179, "acc_stderr": 0.019565236782930893, "acc_norm": 0.8179487179487179, "acc_norm_stderr": 0.019565236782930893 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.43333333333333335, "acc_stderr": 0.030213340289237924, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.030213340289237924 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8403361344537815, "acc_stderr": 0.023793353997528802, "acc_norm": 0.8403361344537815, "acc_norm_stderr": 0.023793353997528802 }, "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": 0.9155963302752294, "acc_stderr": 0.011918819327334872, "acc_norm": 0.9155963302752294, "acc_norm_stderr": 0.011918819327334872 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6620370370370371, "acc_stderr": 0.03225941352631295, "acc_norm": 0.6620370370370371, "acc_norm_stderr": 0.03225941352631295 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9166666666666666, "acc_stderr": 0.019398452135813905, "acc_norm": 0.9166666666666666, "acc_norm_stderr": 0.019398452135813905 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9071729957805907, "acc_stderr": 0.01888975055095671, "acc_norm": 0.9071729957805907, "acc_norm_stderr": 0.01888975055095671 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8071748878923767, "acc_stderr": 0.026478240960489365, "acc_norm": 0.8071748878923767, "acc_norm_stderr": 0.026478240960489365 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8778625954198473, "acc_stderr": 0.028718776889342327, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.028718776889342327 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8842975206611571, "acc_stderr": 0.0291998024556228, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.0291998024556228 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8888888888888888, "acc_stderr": 0.030381596756651655, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.030381596756651655 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5803571428571429, "acc_stderr": 0.04684099321077106, "acc_norm": 0.5803571428571429, "acc_norm_stderr": 0.04684099321077106 }, "harness|hendrycksTest-management|5": { "acc": 0.8932038834951457, "acc_stderr": 0.030581088928331356, "acc_norm": 0.8932038834951457, "acc_norm_stderr": 0.030581088928331356 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253869, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253869 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9067688378033205, "acc_stderr": 0.010397417087292847, "acc_norm": 0.9067688378033205, "acc_norm_stderr": 0.010397417087292847 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8265895953757225, "acc_stderr": 0.020383229551135022, "acc_norm": 0.8265895953757225, "acc_norm_stderr": 0.020383229551135022 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.8, "acc_stderr": 0.013378001241813075, "acc_norm": 0.8, "acc_norm_stderr": 0.013378001241813075 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8398692810457516, "acc_stderr": 0.020998740930362303, "acc_norm": 0.8398692810457516, "acc_norm_stderr": 0.020998740930362303 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8038585209003215, "acc_stderr": 0.022552447780478033, "acc_norm": 0.8038585209003215, "acc_norm_stderr": 0.022552447780478033 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8580246913580247, "acc_stderr": 0.019420260109438293, "acc_norm": 0.8580246913580247, "acc_norm_stderr": 0.019420260109438293 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6276595744680851, "acc_stderr": 0.02883892147125145, "acc_norm": 0.6276595744680851, "acc_norm_stderr": 0.02883892147125145 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5847457627118644, "acc_stderr": 0.012585471793400664, "acc_norm": 0.5847457627118644, "acc_norm_stderr": 0.012585471793400664 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8051470588235294, "acc_stderr": 0.02406059942348742, "acc_norm": 0.8051470588235294, "acc_norm_stderr": 0.02406059942348742 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8104575163398693, "acc_stderr": 0.015856152189980256, "acc_norm": 0.8104575163398693, "acc_norm_stderr": 0.015856152189980256 }, "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": 0.8326530612244898, "acc_stderr": 0.02389714476891452, "acc_norm": 0.8326530612244898, "acc_norm_stderr": 0.02389714476891452 }, "harness|hendrycksTest-sociology|5": { "acc": 0.9104477611940298, "acc_stderr": 0.02019067053502791, "acc_norm": 0.9104477611940298, "acc_norm_stderr": 0.02019067053502791 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072864, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072864 }, "harness|truthfulqa:mc|0": { "mc1": 0.4369645042839657, "mc1_stderr": 0.017363844503195985, "mc2": 0.592598246243346, "mc2_stderr": 0.014870176336077599 }, "harness|winogrande|5": { "acc": 0.8382004735595896, "acc_stderr": 0.010350128010292406 }, "harness|gsm8k|5": { "acc": 0.46171341925701287, "acc_stderr": 0.013732048227016682 } } ``` ## 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]

rafatecno1/rafa

--- license: openrail ---

alvations/c4p0-x1-en-fr

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 4047 num_examples: 2 download_size: 21379 dataset_size: 4047 configs: - config_name: default data_files: - split: train path: 37ded8bbdbf8c054/train-* ---

pib

--- task_categories: - translation - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling multilinguality: - translation language: - bn - en - gu - hi - ml - mr - or - pa - ta - te - ur language_creators: - other annotations_creators: - no-annotation source_datasets: - original size_categories: - 100K<n<1M - 10K<n<100K license: - cc-by-4.0 paperswithcode_id: null pretty_name: CVIT PIB dataset_info: - config_name: or-ur features: - name: translation dtype: translation: languages: - or - ur splits: - name: train num_bytes: 27790211 num_examples: 43766 download_size: 393352875 dataset_size: 27790211 - config_name: ml-or features: - name: translation dtype: translation: languages: - ml - or splits: - name: train num_bytes: 16011549 num_examples: 19413 download_size: 393352875 dataset_size: 16011549 - config_name: bn-ta features: - name: translation dtype: translation: languages: - bn - ta splits: - name: train num_bytes: 28706668 num_examples: 33005 download_size: 393352875 dataset_size: 28706668 - config_name: gu-mr features: - name: translation dtype: translation: languages: - gu - mr splits: - name: train num_bytes: 24253770 num_examples: 30766 download_size: 393352875 dataset_size: 24253770 - config_name: hi-or features: - name: translation dtype: translation: languages: - hi - or splits: - name: train num_bytes: 45086618 num_examples: 61070 download_size: 393352875 dataset_size: 45086618 - config_name: en-or features: - name: translation dtype: translation: languages: - en - or splits: - name: train num_bytes: 51258494 num_examples: 98230 download_size: 393352875 dataset_size: 51258494 - config_name: mr-ur features: - name: translation dtype: translation: languages: - mr - ur splits: - name: train num_bytes: 34053295 num_examples: 49691 download_size: 393352875 dataset_size: 34053295 - config_name: en-ta features: - name: translation dtype: translation: languages: - en - ta splits: - name: train num_bytes: 74931542 num_examples: 118759 download_size: 393352875 dataset_size: 74931542 - 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config_name: hi-ur features: - name: translation dtype: translation: languages: - hi - ur splits: - name: train num_bytes: 81262590 num_examples: 109951 download_size: 393352875 dataset_size: 81262590 - config_name: mr-or features: - name: translation dtype: translation: languages: - mr - or splits: - name: train num_bytes: 33998805 num_examples: 47001 download_size: 393352875 dataset_size: 33998805 - config_name: en-ur features: - name: translation dtype: translation: languages: - en - ur splits: - name: train num_bytes: 100571795 num_examples: 202578 download_size: 393352875 dataset_size: 100571795 - config_name: ml-ur features: - name: translation dtype: translation: languages: - ml - ur splits: - name: train num_bytes: 15663718 num_examples: 20913 download_size: 393352875 dataset_size: 15663718 - config_name: bn-mr features: - name: translation dtype: translation: languages: - bn - mr splits: - name: train num_bytes: 27604502 num_examples: 34043 download_size: 393352875 dataset_size: 27604502 - config_name: gu-ta features: - name: translation dtype: translation: languages: - gu - ta splits: - name: train num_bytes: 25089131 num_examples: 29187 download_size: 393352875 dataset_size: 25089131 - config_name: pa-te features: - name: translation dtype: translation: languages: - pa - te splits: - name: train num_bytes: 23119690 num_examples: 25684 download_size: 393352875 dataset_size: 23119690 - config_name: bn-gu features: - name: translation dtype: translation: languages: - bn - gu splits: - name: train num_bytes: 19899277 num_examples: 25166 download_size: 393352875 dataset_size: 19899277 - config_name: bn-ur features: - name: translation dtype: translation: languages: - bn - ur splits: - name: train num_bytes: 27540215 num_examples: 39290 download_size: 393352875 dataset_size: 27540215 - config_name: ml-mr features: - name: translation dtype: translation: languages: - ml - mr splits: - name: train num_bytes: 19723458 num_examples: 22796 download_size: 393352875 dataset_size: 19723458 - config_name: or-ta features: - name: translation dtype: translation: languages: - or - ta splits: - name: train num_bytes: 35357904 num_examples: 44035 download_size: 393352875 dataset_size: 35357904 - config_name: ta-te features: - name: translation dtype: translation: languages: - ta - te splits: - name: train num_bytes: 17415768 num_examples: 17359 download_size: 393352875 dataset_size: 17415768 - config_name: gu-or features: - name: translation dtype: translation: languages: - gu - or splits: - name: train num_bytes: 20111876 num_examples: 27162 download_size: 393352875 dataset_size: 20111876 - config_name: en-gu features: - name: translation dtype: translation: languages: - en - gu splits: - name: train num_bytes: 33630906 num_examples: 59739 download_size: 393352875 dataset_size: 33630906 - config_name: hi-mr features: - name: translation dtype: translation: languages: - hi - mr splits: - name: train num_bytes: 55680473 num_examples: 69186 download_size: 393352875 dataset_size: 55680473 - config_name: mr-ta features: - name: translation dtype: translation: languages: - mr - ta splits: - name: train num_bytes: 41585343 num_examples: 48535 download_size: 393352875 dataset_size: 41585343 - config_name: en-mr features: - name: translation dtype: translation: languages: - en - mr splits: - name: train num_bytes: 65042597 num_examples: 117199 download_size: 393352875 dataset_size: 65042597 config_names: - bn-en - bn-gu - bn-hi - bn-ml - bn-mr - bn-or - bn-pa - bn-ta - bn-te - bn-ur - en-gu - en-hi - en-ml - en-mr - en-or - en-pa - en-ta - en-te - en-ur - gu-hi - gu-ml - gu-mr - gu-or - gu-pa - gu-ta - gu-te - gu-ur - hi-ml - hi-mr - hi-or - hi-pa - hi-ta - hi-te - hi-ur - ml-mr - ml-or - ml-pa - ml-ta - ml-te - ml-ur - mr-or - mr-pa - mr-ta - mr-te - mr-ur - or-pa - or-ta - or-te - or-ur - pa-ta - pa-te - pa-ur - ta-te - ta-ur - te-ur --- # Dataset Card for CVIT PIB ## 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) - [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:** http://preon.iiit.ac.in/~jerin/bhasha/ - **Paper:** https://arxiv.org/abs/2008.04860 - **Point of Contact:** [Mailing List](cvit-bhasha@googlegroups.com) ### Dataset Summary This dataset is the large scale sentence aligned corpus in 11 Indian languages, viz. CVIT-PIB corpus that is the largest multilingual corpus available for Indian languages. ### Supported Tasks and Leaderboards - Machine Translation ### Languages Parallel data for following languages [en, bn, gu, hi, ml, mr, pa, or, ta, te, ur] are covered. ## Dataset Structure ### Data Instances An example for the "gu-pa" language pair: ``` { 'translation': { 'gu': 'એવો નિર્ણય લેવાયો હતો કે ખંતપૂર્વકની કામગીરી હાથ ધરવા, કાયદેસર અને ટેકનિકલ મૂલ્યાંકન કરવા, વેન્ચર કેપિટલ ઇન્વેસ્ટમેન્ટ સમિતિની બેઠક યોજવા વગેરે એઆઇએફને કરવામાં આવેલ પ્રતિબદ્ધતાના 0.50 ટકા સુધી અને બાકીની રકમ એફએફએસને પૂર્ણ કરવામાં આવશે.', 'pa': 'ਇਹ ਵੀ ਫੈਸਲਾ ਕੀਤਾ ਗਿਆ ਕਿ ਐੱਫਆਈਆਈ ਅਤੇ ਬਕਾਏ ਲਈ ਕੀਤੀਆਂ ਗਈਆਂ ਵਚਨਬੱਧਤਾਵਾਂ ਦੇ 0.50 % ਦੀ ਸੀਮਾ ਤੱਕ ਐੱਫਈਐੱਸ ਨੂੰ ਮਿਲਿਆ ਜਾਏਗਾ, ਇਸ ਨਾਲ ਉੱਦਮ ਪੂੰਜੀ ਨਿਵੇਸ਼ ਕਮੇਟੀ ਦੀ ਬੈਠਕ ਦਾ ਆਯੋਜਨ ਉਚਿਤ ਸਾਵਧਾਨੀ, ਕਾਨੂੰਨੀ ਅਤੇ ਤਕਨੀਕੀ ਮੁੱਲਾਂਕਣ ਲਈ ਸੰਚਾਲਨ ਖਰਚ ਆਦਿ ਦੀ ਪੂਰਤੀ ਹੋਵੇਗੀ।' } } ``` ### Data Fields - `translation`: Translation field containing the parallel text for the pair of languages. ### Data Splits The dataset is in a single "train" split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license. ### Citation Information ``` @inproceedings{siripragada-etal-2020-multilingual, title = "A Multilingual Parallel Corpora Collection Effort for {I}ndian Languages", author = "Siripragada, Shashank and Philip, Jerin and Namboodiri, Vinay P. and Jawahar, C V", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.462", pages = "3743--3751", language = "English", ISBN = "979-10-95546-34-4", } @article{2020, title={Revisiting Low Resource Status of Indian Languages in Machine Translation}, url={http://dx.doi.org/10.1145/3430984.3431026}, DOI={10.1145/3430984.3431026}, journal={8th ACM IKDD CODS and 26th COMAD}, publisher={ACM}, author={Philip, Jerin and Siripragada, Shashank and Namboodiri, Vinay P. and Jawahar, C. V.}, year={2020}, month={Dec} } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset, and [@albertvillanova](https://github.com/albertvillanova) for updating its version.

joey234/mmlu-jurisprudence-dev

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string splits: - name: dev num_bytes: 2700 num_examples: 5 download_size: 6711 dataset_size: 2700 --- # Dataset Card for "mmlu-jurisprudence-dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

result-kand2-sdxl-wuerst-karlo/c06e4969

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 200 num_examples: 10 download_size: 1394 dataset_size: 200 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c06e4969" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

izou3/MaskFormer_DB

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 125142150.192 num_examples: 7968 - name: test num_bytes: 21986379.57 num_examples: 1405 download_size: 21304292 dataset_size: 147128529.762 --- # Dataset Card for "MaskFormer_DB" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vikp/evol_codealpaca_filtered_87k

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: quality_prob dtype: float64 - name: learning_prob dtype: float64 splits: - name: train num_bytes: 194291512.64351812 num_examples: 87705 download_size: 107933444 dataset_size: 194291512.64351812 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "evol_codealpaca_filtered_86k" Filtered version of `theblackcat102/evol-codealpaca-v1`, with manual filtering, and automatic filtering based on quality and learning value classifiers.

bond005/sova_rudevices

--- pretty_name: RuDevices annotations_creators: - expert-generated language_creators: - crowdsourced language: - ru license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: size_categories: - 10K<n<100k source_datasets: - extended task_categories: - automatic-speech-recognition - audio-classification --- # Dataset Card for sova_rudevices ## 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:** [SOVA RuDevices](https://github.com/sovaai/sova-dataset) - **Repository:** [SOVA Dataset](https://github.com/sovaai/sova-dataset) - **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - **Point of Contact:** [SOVA.ai](mailto:support@sova.ai) ### Dataset Summary SOVA Dataset is free public STT/ASR dataset. It consists of several parts, one of them is SOVA RuDevices. This part is an acoustic corpus of approximately 100 hours of 16kHz Russian live speech with manual annotating, prepared by [SOVA.ai team](https://github.com/sovaai). Authors do not divide the dataset into train, validation and test subsets. Therefore, I was compelled to prepare this splitting. The training subset includes more than 82 hours, the validation subset includes approximately 6 hours, and the test subset includes approximately 6 hours too. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. ### Languages The audio is in Russian. ## Dataset Structure ### Data Instances A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided. ``` {'audio': {'path': '/home/bond005/datasets/sova_rudevices/data/train/00003ec0-1257-42d1-b475-db1cd548092e.wav', 'array': array([ 0.00787354, 0.00735474, 0.00714111, ..., -0.00018311, -0.00015259, -0.00018311]), dtype=float32), 'sampling_rate': 16000}, 'transcription': 'мне получше стало'} ``` ### Data Fields - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - transcription: the transcription of the audio file. ### Data Splits This dataset consists of three splits: training, validation, and test. This splitting was realized with accounting of internal structure of SOVA RuDevices (the validation split is based on the subdirectory `0`, and the test split is based on the subdirectory `1` of the original dataset), but audio recordings of the same speakers can be in different splits at the same time (the opposite is not guaranteed). | | Train | Validation | Test | | ----- | ------ | ---------- | ----- | | examples | 81607 | 5835 | 5799 | | hours | 82.4h | 5.9h | 5.8h | ## 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 All recorded audio files were manually annotated. #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Egor Zubarev, Timofey Moskalets, and SOVA.ai team. ### Licensing Information [Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @misc{sova2021rudevices, author = {Zubarev, Egor and Moskalets, Timofey and SOVA.ai}, title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech}, publisher = {GitHub}, journal = {GitHub repository}, year = {2021}, howpublished = {\url{https://github.com/sovaai/sova-dataset}}, } ``` ### Contributions Thanks to [@bond005](https://github.com/bond005) for adding this dataset.

AleAle2423/Table_of_contents

--- license: mit language: - en tags: - synthetic pretty_name: Table of contents data set size_categories: - 10K<n<100K ---

KaiLv/UDR_CosmosQA

--- dataset_info: features: - name: idx dtype: int64 - name: question dtype: string - name: label dtype: string - name: choices dtype: string - name: len_question dtype: int64 - name: max_len_choices dtype: int64 splits: - name: train num_bytes: 11188271 num_examples: 18770 - name: test num_bytes: 3979297 num_examples: 6030 - name: validation num_bytes: 1722925 num_examples: 2603 - name: debug num_bytes: 2985534 num_examples: 5000 download_size: 11095169 dataset_size: 19876027 --- # Dataset Card for "UDR_CosmosQA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nlpyeditepe/tr-qnli

--- annotations_creators: - found language_creators: - machine-generated language: - tr-TR license: - mit multilinguality: - monolingual pretty_name: QNLI for Turkish size_categories: - unknown source_datasets: - extended|glue task_categories: - text-classification task_ids: - natural-language-inference ---

anan-2024/twitter_dataset_1713009432

--- 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: 24140 num_examples: 56 download_size: 12841 dataset_size: 24140 configs: - config_name: default data_files: - split: train path: data/train-* ---

chmourya/openassistant-mourya

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1797495 num_examples: 1500 download_size: 1030938 dataset_size: 1797495 configs: - config_name: default data_files: - split: train path: data/train-* ---

salgadev/spirit-patterns

--- license: lgpl task_categories: - image-classification tags: - chemistry - spirits - beverages size_categories: - n<1K language: - en pretty_name: SpiritPatterns ---

dubeyam/PairRM-Preference-LIMA-Dataset

--- license: mit dataset_info: features: - name: data struct: - name: '##Question' dtype: string - name: '##Best Answer' dtype: string - name: '##Worst Answer' dtype: string splits: - name: train num_bytes: 44058 num_examples: 50 download_size: 33801 dataset_size: 44058 configs: - config_name: default data_files: - split: train path: data/train-* ---

terru3/tokenized_tinystories384

--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 5998238715 num_examples: 2119719 - name: validation num_bytes: 61703030 num_examples: 21990 download_size: 1771825341 dataset_size: 6059941745 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

ymoslem/Law-StackExchange

--- license: cc-by-sa-4.0 task_categories: - question-answering - text-classification - sentence-similarity language: - en tags: - legal pretty_name: Law Stack Exchange Questions and Answers size_categories: - 10K<n<100K --- All StackExchange legal questions and their answers from the Law site, up to 14 August 2023. The repository includes a notebook for the process using the official StackExchange API.

Brizape/Variome_0404

--- dataset_info: features: - name: id dtype: string - name: texts dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 splits: - name: train num_bytes: 8210534 num_examples: 96 - name: test num_bytes: 1447445 num_examples: 24 download_size: 972009 dataset_size: 9657979 --- # Dataset Card for "Variome_0404" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ekim15/bone_marrow_cell_dataset

--- license: cc-by-4.0 dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ABE '1': ART '2': BAS '3': BLA '4': EBO '5': EOS '6': FGC '7': HAC '8': KSC '9': LYI '10': LYT '11': MMZ '12': MON '13': MYB '14': NGB '15': NGS '16': NIF '17': OTH '18': PEB '19': PLM '20': PMO splits: - name: train num_bytes: 5531894343.482 num_examples: 137093 - name: validation num_bytes: 688690986.192 num_examples: 17146 - name: test num_bytes: 691641698.035 num_examples: 17135 download_size: 6935845206 dataset_size: 6912227027.709001 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* task_categories: - image-classification - unconditional-image-generation tags: - biology - medical size_categories: - 100K<n<1M --- ## About This Dataset Bone marrow biopsy is procedure applied to collect and examine bone marrow — the spongy tissue inside some of your larger bones. This biopsy can show whether your bone marrow is healthy and making normal amounts of blood cells. Doctors use these procedures to diagnose and monitor blood and marrow diseases, cancers, as well as fevers of unknown origin. The dataset contains a collection of over 170,000 de-identified, expert-annotated cells from the bone marrow smears of 945 patients stained using the May-Grünwald-Giemsa/Pappenheim stain. The diagnosis distribution in the cohort included a variety of hematological diseases reflective of the sample entry of a large laboratory specialized in leukemia diagnostics. Image acquisition was performed using a brightfield microscope with 40x magnification and oil immersion. All samples were processed in the Munich Leukemia Laboratory (MLL), scanned using equipment developed at Fraunhofer IIS and post-processed using software developed at Helmholtz Munich. ## How to Use this dataset - Create a multi-classification model to predict cell abnormalities; - Create a binary-classification model to predict if a cell is normal or not. - Create image generation model to add to training datset ## Acknowledgements Citation Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., & Marr, C. (2021). An Expert-Annotated Dataset of Bone Marrow Cytology in Hematologic Malignancies [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.AXH3-T579 Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., and Marr, C. (2021). Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image dataset. https://doi.org/10.1182/blood.2020010568 License CC BY 4.0 https://www.kaggle.com/datasets/andrewmvd/bone-marrow-cell-classification/data

neohack22/temporary-dataset

--- license: apache-2.0 ---

GabrielVidal/dalle-3-palette

--- dataset_info: features: - name: image dtype: image - name: palette dtype: image - name: text dtype: string splits: - name: train num_bytes: 1778362423 num_examples: 1000 download_size: 1778260113 dataset_size: 1778362423 configs: - config_name: default data_files: - split: train path: data/train-* license: cc0-1.0 language: - en size_categories: - n<1K tags: - image-text-dataset - synthetic-dataset ---

huggingnft/boredapeyachtclub

--- tags: - huggingnft - nft - huggan - gan - image - images task: - unconditional-image-generation datasets: - huggingnft/boredapeyachtclub license: mit --- # Dataset Card ## Disclaimer All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder. ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft) - **Repository:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft) - **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) ### Dataset Summary NFT images dataset for unconditional generation. NFT collection available [here](https://opensea.io/collection/boredapeyachtclub). Model is available [here](https://huggingface.co/huggingnft/boredapeyachtclub). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingnft/boredapeyachtclub") ``` ## Dataset Structure [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Fields The data fields are the same among all splits. - `image`: an `image` feature. - `id`: an `int` feature. - `token_metadata`: a `str` feature. - `image_original_url`: a `str` feature. ### Data Splits [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 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{huggingnft, author={Aleksey Korshuk} year=2022 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft)

renumics/f1_demo_dataset

--- dataset_info: features: - name: Time dtype: duration[ns] - name: Driver dtype: string - name: DriverNumber dtype: string - name: LapTime dtype: float64 - name: LapNumber dtype: float64 - name: Stint dtype: float64 - name: PitOutTime dtype: duration[ns] - name: PitInTime dtype: duration[ns] - name: Sector1Time dtype: float64 - name: Sector2Time dtype: float64 - name: Sector3Time dtype: float64 - name: Sector1SessionTime dtype: duration[ns] - name: Sector2SessionTime dtype: duration[ns] - name: Sector3SessionTime dtype: duration[ns] - name: SpeedI1 dtype: float64 - name: SpeedI2 dtype: float64 - name: SpeedFL dtype: float64 - name: SpeedST dtype: float64 - name: IsPersonalBest dtype: bool - name: Compound dtype: string - name: TyreLife dtype: float64 - name: FreshTyre dtype: bool - name: Team dtype: string - name: LapStartTime dtype: duration[ns] - name: LapStartDate dtype: timestamp[ns] - name: TrackStatus dtype: string - name: Position dtype: float64 - name: Deleted dtype: bool - name: DeletedReason dtype: string - name: FastF1Generated dtype: bool - name: IsAccurate dtype: bool - name: speed sequence: sequence: float64 - name: throttle sequence: sequence: float64 - name: drs sequence: sequence: float64 - name: nGear sequence: sequence: float64 - name: brake sequence: sequence: float64 - name: x sequence: sequence: float64 - name: y sequence: sequence: float64 - name: z sequence: sequence: float64 - name: distance_driver sequence: sequence: float64 - name: speed_emb sequence: float64 - name: brake_emb sequence: float64 - name: throttle_emb sequence: float64 - name: x_emb dtype: float64 - name: y_emb dtype: float64 - name: z_emb dtype: float64 - name: gear_vis dtype: string - name: speed_vis dtype: string - name: portrait dtype: string - name: brake_emb_reduced sequence: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 22426400 num_examples: 201 download_size: 15371945 dataset_size: 22426400 --- # Dataset Card for "f1_demo_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rcds/lower_court_insertion_swiss_judgment_prediction

--- annotations_creators: - expert-generated language: - de - fr - it - en language_creators: - expert-generated - found license: - cc-by-sa-4.0 multilinguality: - multilingual pretty_name: LowerCourtInsertionSwissJudgmentPrediction size_categories: - 1K<n<10K source_datasets: - extended|swiss_judgment_prediction tags: - explainability-judgment-prediction task_categories: - text-classification - other task_ids: [] --- # Dataset Card for "LowerCourtInsertionSwissJudgmentPrediction": An implementation of lower court insertion bias analysis for Swiss judgment prediction ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Summary](#dataset-summary) - [Documents](#documents) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset **str**ucture](#dataset-**str**ucture) - [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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Summary This dataset contains an implementation of lower-court-insertion for the SwissJudgmentPrediction task. Note that this dataset only provides a test set and should be used in comination with the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset. ### Documents Lower-Court-Insertion-Swiss-Judgment-Prediction is a subset of the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset. The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Lower-Court-Insertion-Swiss-Judgment-Prediction extends this dataset by adding lower court insertion. ### Supported Tasks and Leaderboards LowerCourtInsertionSwissJudgmentPrediction can be used for performing the LowerCourtInsertion in the legal judgment prediction task. ### Languages Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings. ## Dataset structure ### Data Instances #### Multilingual use of the dataset When the dataset is used in a multilingual setting selecting the the 'all' flag: ```python from datasets import load_dataset dataset = load_dataset('rcds/lower_court_insertion_swiss_judgment_prediction', 'all') ``` #### Monolingual use of the dataset When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example: ```python from datasets import load_dataset dataset = load_dataset('rcds/lower-court-insertion_swiss_judgment_prediction', 'de') ``` ### Data Fields The following data fields are provided for documents (test): id: (**int**) a unique identifier of the for the document<br/> year: (**int**) the publication year<br/> label: (**str**) the judgment outcome: dismissal or approval<br/> language: (**str**) one of (de, fr, it)<br/> region: (**str**) the region of the lower court<br/> canton: (**str**) the canton of the lower court<br/> legal area: (**str**) the legal area of the case<br/> explainability_label: (**str**) the explainability label assigned to the occluded text: (Lower court, Baseline)<br/> text: (**str**) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)<br/> lower_court: (**str**) the inserted lower_court (for Baseline there is no insertion)<br/> ### Data Splits (Including Swiss Judgment Prediction) Language | Subset | Number of Rows (Test) |-----|-----|------| German| de| __378__ French | fr| __414__ Italian | it| __335__ All | all | __1127__ Language | Subset | Number of Documents (Test) | ----------- | ----------- | ----------- | German| de | __38__ French | fr | __36__ Italian | it | __34__ All | all | __108__ ## Dataset Creation ### Curation Rationale The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner. ### Source Data #### Initial Data Collection and Normalization The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. #### Who are the source language producers? Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition the a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts with the lower court. These lower court annotations were then use the insert each lower court into each case once (instead of the original lower court). Allowing an analysis of the changes in the models performance for each inserted lower court, giving insight into a possible bias among them. The legal expert annotation were conducted from April 2020 to August 2020. #### Who are the annotators? Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level). ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html. ## Additional Information ### Dataset Curators Niklaus et al. (2021) and Nina Baumgartner ### Licensing Information We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf) © Swiss Federal Supreme Court, 2000-2020 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf ### Citation Information ``` @misc{baumgartner_nina_occlusion_2019, title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland}, shorttitle = {From Occlusion to Transparancy}, abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.}, author = {{Baumgartner, Nina}}, year = {2022}, langid = {english} } ``` ### Contributions Thanks to [@ninabaumgartner](https://github.com/ninabaumgartner) for adding this dataset.

OpenGVLab/Caption-Evaluation-Data

--- license: apache-2.0 --- This repository presents the evaluation data used for [ASM](https://github.com/Weiyun1025/all-seeing-official/tree/main/all-seeing). Please refer to [this document](https://github.com/OpenGVLab/all-seeing/tree/main/all-seeing#testing) for more details about the repository.

HumanCompatibleAI/random-seals-Walker2d-v1

--- dataset_info: features: - name: obs sequence: sequence: float64 - name: acts sequence: sequence: float32 - name: infos sequence: string - name: terminal dtype: bool - name: rews sequence: float32 splits: - name: train num_bytes: 81303050 num_examples: 100 download_size: 41495120 dataset_size: 81303050 --- # Dataset Card for "random-seals-Walker2d-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zolak/twitter_dataset_50_1713123422

--- 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: 244794 num_examples: 572 download_size: 121577 dataset_size: 244794 configs: - config_name: default data_files: - split: train path: data/train-* ---

Oztobuzz/gsm8k_0.1_42

--- license: mit ---

rkorez/medix-codegen-v2

--- dataset_info: features: - name: index dtype: int64 - name: repo_id dtype: string - name: file_path dtype: string - name: content dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 206046946 num_examples: 30000 download_size: 61887714 dataset_size: 206046946 configs: - config_name: default data_files: - split: train path: data/train-* ---

QNN/autotrain-data-pubmed

--- task_categories: - token-classification --- # AutoTrain Dataset for project: pubmed ## Dataset Description This dataset has been automatically processed by AutoTrain for project pubmed. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "tokens": [ "Pd", "has", "been", "regarded", "as", "one", "of", "the", "alternatives", "to", "Pt", "as", "a", "promising", "hydrogen", "evolution", "reaction", "(HER)", "catalyst.", "Strategies", "including", "Pd-metal", "alloys", "(Pd-M)", "and", "Pd", "hydrides", "(PdH<sub><i>x</i></sub>)", "have", "been", "proposed", "to", "boost", "HER", "performances.", "However,", "the", "stability", "issues,", "e.g.,", "the", "dissolution", "in", "Pd-M", "and", "the", "hydrogen", "releasing", "in", "PdH<sub><i>x</i></sub>,", "restrict", "the", "industrial", "application", "of", "Pd-based", "HER", "catalysts.", "We", "here", "design", "and", "synthesize", "a", "stable", "Pd-Cu", "hydride", "(", "PdCu<sub>0.2</sub>H<sub>0.43</sub>", ")", "catalyst,", "combining", "the", "advantages", "of", "both", "Pd-M", "and", "PdH<sub><i>x</i></sub>", "structures", "and", "improving", "the", "HER", "durability", "simultaneously.", "The", "hydrogen", "intercalation", "is", "realized", "under", "atmospheric", "pressure", "(1.0", "atm)", "following", "our", "synthetic", "approach", "that", "imparts", "high", "stability", "to", "the", "Pd-Cu", "hydride", "structure.", "The", "obtained", "PdCu<sub>0.2</sub>H<sub>0.43</sub>", "catalyst", "exhibits", "a", "small", "overpotential", "of", "28", "mV", "at", "10", "mA/cm<sup>2</sup>", ",", "a", "low", "Tafel", "slope", "of", "23", "mV/dec", ",", "and", "excellent", "HER", "durability", "due", "to", "its", "appropriate", "hydrogen", "adsorption", "free", "energy", "and", "alleviated", "metal", "dissolution", "rate.", "</p>", "<p>" ], "tags": [ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 4, 2, 5, 5, 2, 5, 5, 2, 2, 2, 4, 2, 2, 5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ] }, { "tokens": [ "A", "critical", "challenge", "in", "energy", "research", "is", "the", "development", "of", "earth", "abundant", "and", "cost-effective", "materials", "that", "catalyze", "the", "electrochemical", "splitting", "of", "water", "into", "hydrogen", "and", "oxygen", "at", "high", "rates", "and", "low", "overpotentials.", "Key", "to", "addressing", "this", "issue", "lies", "not", "only", "in", "the", "synthesis", "of", "new", "materials,", "but", "also", "in", "the", "elucidation", "of", "their", "active", "sites,", "their", "structure", "under", "operating", "conditions", "and", "ultimately,", "extraction", "of", "the", "structure-function", "relationships", "used", "to", "spearhead", "the", "next", "generation", "of", "catalyst", "development.", "In", "this", "work,", "we", "present", "a", "complete", "cycle", "of", "synthesis,", "operando", "characterization,", "and", "redesign", "of", "an", "amorphous", "cobalt", "phosphide", "(", "CoP", "<sub><i>x</i></sub>", ")", "bifunctional", "catalyst.", "The", "research", "was", "driven", "by", "integrated", "electrochemical", "analysis,", "Raman", "spectroscopy", "and", "gravimetric", "measurements", "utilizing", "a", "novel", "quartz", "crystal", "microbalance", "spectroelectrochemical", "cell", "to", "uncover", "the", "catalytically", "active", "species", "of", "amorphous", "CoP", "<sub><i>x</i></sub>", "and", "subsequently", "modify", "the", "material", "to", "enhance", "the", "activity", "of", "the", "elucidated", "catalytic", "phases.", "Illustrating", "the", "power", "of", "our", "approach,", "the", "second", "generation", "cobalt-iron", "phosphide", "(", "CoFeP<sub>x</sub>", ")", "catalyst,", "developed", "through", "an", "iteration", "of", "the", "operando", "measurement", "directed", "optimization", "cycle,", "is", "superior", "in", "both", "hydrogen", "and", "oxygen", "evolution", "reactivity", "over", "the", "previous", "material", "and", "is", "capable", "of", "overall", "water", "electrolysis", "at", "a", "current", "density", "of", "10", "mA", "cm<sup>-2</sup>", "with", "1.5", "V", "applied", "bias", "in", "1", "M", "KOH", "electrolyte", "solution.", "</p>", "<p>" ], "tags": [ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2, 5, 5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ] } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)", "tags": "Sequence(feature=ClassLabel(names=['CATALYST', 'CO-CATALYST', 'O', 'Other', 'PROPERTY_NAME', 'PROPERTY_VALUE'], id=None), length=-1, id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 166 | | valid | 44 |

ssahir/REPV

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: path dtype: string - name: file dtype: string - name: gender dtype: string - name: emotion dtype: string - name: speech sequence: float32 splits: - name: train num_bytes: 380197186 num_examples: 1628 - name: test num_bytes: 92682047 num_examples: 407 download_size: 0 dataset_size: 472879233 --- # Dataset Card for "REPV" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

felixsaaro/test

--- dataset_info: features: - name: image dtype: image - name: name dtype: string - name: frameType dtype: string splits: - name: train num_bytes: 660338115.545 num_examples: 12405 download_size: 656146541 dataset_size: 660338115.545 license: apache-2.0 task_categories: - text-to-image language: - en pretty_name: Yugio Cards size_categories: - 1K<n<10K --- # Dataset Card for "test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ISTNetworks/open-orca-arabic

--- license: other license_name: ist license_link: LICENSE ---

AlekseyKorshuk/full_user_edit_responses-clean

--- dataset_info: features: - name: message_id dtype: string - name: model_input dtype: string - name: response dtype: string - name: edited_response dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 782450796.338342 num_examples: 364272 download_size: 355765300 dataset_size: 782450796.338342 --- # Dataset Card for "full_user_edit_responses-clean" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zolak/twitter_dataset_81_1713170079

--- 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: 431921 num_examples: 996 download_size: 201715 dataset_size: 431921 configs: - config_name: default data_files: - split: train path: data/train-* ---

ranimeree/NewDataSetMixed

--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 395458124.688 num_examples: 2769 - name: validation num_bytes: 61731350.0 num_examples: 352 - name: test num_bytes: 12103649.0 num_examples: 101 download_size: 452227024 dataset_size: 469293123.688 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

genvision-ai/test_2024_02_20

--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: segments_info list: - name: area dtype: float64 - name: bbox sequence: float64 - name: category_id dtype: int64 - name: id dtype: int64 - name: image_id dtype: int64 - name: iscrowd dtype: int64 - name: segmentation sequence: sequence: float64 - name: image_name dtype: string splits: - name: train num_bytes: 2721373626.738 num_examples: 12039 download_size: 2737790368 dataset_size: 2721373626.738 configs: - config_name: default data_files: - split: train path: data/train-* ---

lolechka/guanaco-llama2-ru-1k

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 166211.8171846435 num_examples: 788 - name: test num_bytes: 9932.181467181466 num_examples: 46 download_size: 1056715 dataset_size: 176143.99865182498 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

nimrita/BhagwadGitaChapter2Embeddings

--- license: afl-3.0 --- This file contains embeddings of English text of Bhagwad Gita Chapter 2 from this site (https://vedabase.io/en/library/bg/2/) generated using sentence-transformer (sentence-transformers/all-MiniLM-L6-v2) at Hugging Face.

retkowski/ytseg

--- license: cc-by-nc-sa-4.0 language: - en tags: - text segmentation - smart chaptering - segmentation - youtube - asr pretty_name: YTSeg size_categories: - 10K<n<100K task_categories: - token-classification - automatic-speech-recognition --- # From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions We present <span style="font-variant:small-caps; font-weight:700;">YTSeg</span>, a topically and structurally diverse benchmark for the text segmentation task based on YouTube transcriptions. The dataset comprises 19,299 videos from 393 channels, amounting to 6,533 content hours. The topics are wide-ranging, covering domains such as science, lifestyle, politics, health, economy, and technology. The videos are from various types of content formats, such as podcasts, lectures, news, corporate events \& promotional content, and, more broadly, videos from individual content creators. We refer to the **paper** ([acl](https://aclanthology.org/2024.eacl-long.25/) | [arXiv](https://arxiv.org/abs/2402.17633)) for further information. We provide both text and audio data as well as a download script for the video data. ## Data Overview ### <span style="font-variant:small-caps;">YTSeg</span> Each video is represented as a JSON object with the following fields: | Field | Description | |--------------|------------------------------------------------| | `text` | A flat list of sentences. | | `targets` | The target segmentation as string of binary values (e.g., `000100000010`). | | `channel_id` | The YouTube channel ID which this video belongs to. | | `video_id` | The YouTube video ID. | | `audio_path` | Path to the .mp3 file of the video. | | Partition | # Examples | |------------|--------------| | Training | 16,404 (85%) | | Validation | 1,447 (7.5%) | | Testing | 1,448 (7.5%) | | Total | 19,229 | ### <span style="font-variant:small-caps;">YTSeg[Titles]</span> Each chapter of a video is represented as a JSON object with the following fields: | Field | Description | |--------------|------------------------------------------------| | `input` | The complete chapter/section text. | | `input_with_chapters` | The complete chapter/section text with previous section titles prepended. | | `target` | The target chapter title. | | `channel_id` | The YouTube channel ID which this chapter's video belongs to. | | `video_id` | The YouTube video ID which this chapter belongs to. | | `chapter_idx` | The index and placement of the chapter in the video (e.g., the first chapter has index `0`). | | Partition | # Examples | |------------|--------------| | Training | 146,907 (84.8%)| | Validation | 13,206 (7.6%) | | Testing | 13,082 (7.6%) | | Total | 173,195 | ### Audio Data We provide audio files for all examples in the dataset, preprocessed into the .mp3 format with a standardized sample rate of 16,000 Hz and a single channel (mono). These files are organized within the directory structure as follows: `data/audio/<channel_id>/<video_id>.mp3`. ### Video Data A download script for the video and audio data is provided. ```py python download_videos.py ``` In the script, you can further specify a target folder (default is `./video`) and target formats in a priority list. ## Loading Text Data This repository comes with a simple, exemplary script to read in the text data with `pandas`. ```py from load_data import get_partition test_data = get_partition('test') ``` Equivalently, to read in <span style="font-variant:small-caps;">YTSeg[Titles]</span>: ```py from load_data import get_title_partition test_data = get_title_partition('test') ``` ## Citing We kindly request you to cite our corresponding EACL 2024 paper if you use our dataset. ``` @inproceedings{retkowski-waibel-2024-text, title = "From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions", author = "Retkowski, Fabian and Waibel, Alexander", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-long.25", pages = "406--419", abstract = "Text segmentation is a fundamental task in natural language processing, where documents are split into contiguous sections. However, prior research in this area has been constrained by limited datasets, which are either small in scale, synthesized, or only contain well-structured documents. In this paper, we address these limitations by introducing a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. As part of this work, we introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines. Lastly, we expand the notion of text segmentation to a more practical {``}smart chaptering{''} task that involves the segmentation of unstructured content, the generation of meaningful segment titles, and a potential real-time application of the models.", } ``` ## License The dataset is available under the **Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) 4.0** license. We note that we do not own the copyright of the videos and as such opted to release the dataset with a non-commercial license, with the intended use to be in research and education.

Multimodal-Fatima/VQAv2_test_no_image_split_3

--- dataset_info: features: - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: id_image dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: Attributes_ViT_L_14_descriptors_text_davinci_003_full sequence: string - name: clip_tags_ViT_L_14_wo_openai sequence: string - name: clip_tags_ViT_L_14_with_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_with_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_with_openai sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_module sequence: string - name: captions_module_filter sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string splits: - name: test num_bytes: 2150238682 num_examples: 44779 download_size: 540758200 dataset_size: 2150238682 --- # Dataset Card for "VQAv2_test_no_image_split_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686017

--- type: predictions tags: - autotrain - evaluation datasets: - glue eval_info: task: natural_language_inference model: Jiva/xlm-roberta-large-it-mnli metrics: [] dataset_name: glue dataset_config: mnli dataset_split: validation_matched col_mapping: text1: premise text2: hypothesis target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Natural Language Inference * Model: Jiva/xlm-roberta-large-it-mnli * Dataset: glue * Config: mnli * Split: validation_matched To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

Brendan/BabyLMDemo

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2220547 num_examples: 80000 download_size: 0 dataset_size: 2220547 --- # Dataset Card for "BabyLMDemo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Cleudemir/basedevoz2

--- license: openrail ---

yazan-bawab/ps-llm

--- license: mit ---

tmnam20/ViPubMed

--- license: cc language: - vi - en task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: pubmed dataset_info: features: - name: en dtype: string - name: vi dtype: string splits: - name: pubmed22 num_bytes: 44360028980 num_examples: 20087006 download_size: 23041004247 dataset_size: 44360028980 --- # ALERT: This dataset repo is duplicated from [VietAI/vi_pubmed](https://huggingface.co/datasets/VietAI/vi_pubmed) The reason to have this duplicated repo is to avoid the lost/corruption of the original repo when I am doing some stuff ^^. # Dataset Summary 20M Vietnamese PubMed biomedical abstracts translated by the [state-of-the-art English-Vietnamese Translation project](https://arxiv.org/abs/2210.05610). The data has been used as unlabeled dataset for [pretraining a Vietnamese Biomedical-domain Transformer model](https://arxiv.org/abs/2210.05598). ![image](https://user-images.githubusercontent.com/44376091/200204462-4d559113-5bdf-4cc5-9e88-70abe82babba.png) image source: [Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation](https://arxiv.org/abs/2210.05598) # Language - English: Original biomedical abstracts from [Pubmed](https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html) - Vietnamese: Synthetic abstract translated by a [state-of-the-art English-Vietnamese Translation project](https://arxiv.org/abs/2210.05610) # Dataset Structure - The English sequences are - The Vietnamese sequences are # Source Data - Initial Data Collection and Normalization https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html # Licensing Information [Courtesy of the U.S. National Library of Medicine.](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html) # Citation ``` @misc{mtet, doi = {10.48550/ARXIV.2210.05610}, url = {https://arxiv.org/abs/2210.05610}, author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {MTet: Multi-domain Translation for English and Vietnamese}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` ``` @misc{vipubmed, doi = {10.48550/ARXIV.2210.05598}, url = {https://arxiv.org/abs/2210.05598}, author = {Phan, Long and Dang, Tai and Tran, Hieu and Phan, Vy and Chau, Lam D. and Trinh, Trieu H.}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

cbt

--- annotations_creators: - machine-generated language_creators: - found language: - en license: - gfdl multilinguality: - monolingual size_categories: - 100K<n<1M - n<1K source_datasets: - original task_categories: - other - question-answering task_ids: - multiple-choice-qa paperswithcode_id: cbt pretty_name: Children’s Book Test (CBT) config_names: - CN - NE - P - V - raw dataset_info: - config_name: CN features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 301730151 num_examples: 120769 - name: test num_bytes: 6138376 num_examples: 2500 - name: validation num_bytes: 4737257 num_examples: 2000 download_size: 31615166 dataset_size: 312605784 - config_name: NE features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 253551931 num_examples: 108719 - name: test num_bytes: 5707734 num_examples: 2500 - name: validation num_bytes: 4424316 num_examples: 2000 download_size: 29693075 dataset_size: 263683981 - config_name: P features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 852852601 num_examples: 334030 - name: test num_bytes: 6078048 num_examples: 2500 - name: validation num_bytes: 4776981 num_examples: 2000 download_size: 43825356 dataset_size: 863707630 - config_name: V features: - name: sentences sequence: string - name: question dtype: string - name: answer dtype: string - name: options sequence: string splits: - name: train num_bytes: 252177649 num_examples: 105825 - name: test num_bytes: 5806625 num_examples: 2500 - name: validation num_bytes: 4556425 num_examples: 2000 download_size: 29992082 dataset_size: 262540699 - config_name: raw features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 25741580 num_examples: 98 - name: test num_bytes: 1528704 num_examples: 5 - name: validation num_bytes: 1182657 num_examples: 5 download_size: 16350790 dataset_size: 28452941 configs: - config_name: CN data_files: - split: train path: CN/train-* - split: test path: CN/test-* - split: validation path: CN/validation-* - config_name: NE data_files: - split: train path: NE/train-* - split: test path: NE/test-* - split: validation path: NE/validation-* - config_name: P data_files: - split: train path: P/train-* - split: test path: P/test-* - split: validation path: P/validation-* - config_name: V data_files: - split: train path: V/train-* - split: test path: V/test-* - split: validation path: V/validation-* - config_name: raw data_files: - split: train path: raw/train-* - split: test path: raw/test-* - split: validation path: raw/validation-* --- # Dataset Card for CBT ## 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:**[The bAbI project](https://research.fb.com/downloads/babi/) - **Repository:** - **Paper:** [arXiv Paper](https://arxiv.org/pdf/1511.02301.pdf) - **Leaderboard:** - **Point of Contact:** [Felix Hill](mailto:felix.hill@cl.cam.ac.uk) or [Antoine Bordes](mailto:abordes@fb.com). ### Dataset Summary The Children’s Book Test (CBT) is designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available. This dataset contains four different configurations: - `V`: where the answers to the questions are verbs. - `P`: where the answers to the questions are pronouns. - `NE`: where the answers to the questions are named entities. - `CN`: where the answers to the questions are common nouns. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The data is present in English language as written by authors Lucy Maud Montgomery, Charles Dickens,Andrew Lang, etc. in story books for children. ## Dataset Structure ### Data Instances An instance from the `V` config: ``` {'answer': 'said', 'options': ['christening', 'existed', 'hear', 'knows', 'read', 'remarked', 'said', 'sitting', 'talking', 'wearing'], 'question': "`` They are very kind old ladies in their way , '' XXXXX the king ; `` and were nice to me when I was a boy . ''", 'sentences': ['This vexed the king even more than the queen , who was very clever and learned , and who had hated dolls when she was a child .', 'However , she , too in spite of all the books she read and all the pictures she painted , would have been glad enough to be the mother of a little prince .', 'The king was anxious to consult the fairies , but the queen would not hear of such a thing .', 'She did not believe in fairies : she said that they had never existed ; and that she maintained , though The History of the Royal Family was full of chapters about nothing else .', 'Well , at long and at last they had a little boy , who was generally regarded as the finest baby that had ever been seen .', 'Even her majesty herself remarked that , though she could never believe all the courtiers told her , yet he certainly was a fine child -- a very fine child .', 'Now , the time drew near for the christening party , and the king and queen were sitting at breakfast in their summer parlour talking over it .', 'It was a splendid room , hung with portraits of the royal ancestors .', 'There was Cinderella , the grandmother of the reigning monarch , with her little foot in her glass slipper thrust out before her .', 'There was the Marquis de Carabas , who , as everyone knows , was raised to the throne as prince consort after his marriage with the daughter of the king of the period .', 'On the arm of the throne was seated his celebrated cat , wearing boots .', 'There , too , was a portrait of a beautiful lady , sound asleep : this was Madame La Belle au Bois-dormant , also an ancestress of the royal family .', 'Many other pictures of celebrated persons were hanging on the walls .', "`` You have asked all the right people , my dear ? ''", 'said the king .', "`` Everyone who should be asked , '' answered the queen .", "`` People are so touchy on these occasions , '' said his majesty .", "`` You have not forgotten any of our aunts ? ''", "`` No ; the old cats ! ''", "replied the queen ; for the king 's aunts were old-fashioned , and did not approve of her , and she knew it ."]} ``` ### Data Fields For the `raw` config, the data fields are: - `title`: a `string` feature containing the title of the book present in the dataset. - `content`: a `string` feature containing the content of the book present in the dataset. For all other configs, the data fields are: - `sentences`: a `list` of `string` features containing 20 sentences from a book. - `question`: a `string` feature containing a question with blank marked as `XXXX` which is to be filled with one of the options. - `answer`: a `string` feature containing the answer. - `options`: a `list` of `string` features containing the options for the question. ### Data Splits The splits and corresponding sizes are: | |train |test |validation| |:--|------:|----:|---------:| |raw|98 |5 |5 | |V |105825 |2500 |2000 | |P |334030 |2500 |2000 | |CN |120769 |2500 |2000 | |NE |108719 |2500 |2000 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Children's Book Authors ### Annotations #### Annotation process From the [homepage](https://research.fb.com/downloads/babi/): >After allocating books to either training, validation or test sets, we formed example ‘questions’ from chapters in the book by enumerating 21 consecutive sentences. In each question, the first 20 sentences form the context, and a word is removed from the 21st sentence, which becomes the query. Models must identify the answer word among a selection of 10 candidate answers appearing in the context sentences and the query. For finer-grained analyses, we evaluated four classes of question by removing distinct types of word: Named Entities, (Common) Nouns, Verbs and Prepositions. #### 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 ``` GNU Free Documentation License v1.3 ``` ### Citation Information ``` @misc{hill2016goldilocks, title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations}, author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston}, year={2016}, eprint={1511.02301}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.

marhensa/comfyui-workflow

--- language: - en tags: - comfyui - template - workflow --- # HOW TO USE: Drag and Drop Template.JSON / Template.PNG into [ComfyUI in browser](https://github.com/comfyanonymous/ComfyUI) to load the template! (Yes even output PNG file works as workflow template). # PREQUISITE INSTALLATION NOTES: It requires custom nodes and file requirements, it's easy: 1. ["ComfyUI Manager"](https://github.com/ltdrdata/ComfyUI-Manager#installation) (install it first). 2. Open ComfyUI WebUI > Manager > Install Custom Nodes, search "ComfyUI-Impact-Pack", "SDXL Prompt Sytler", and "Recommended SDXL Resolution Calculator", "Masquerade Nodes", Install ALL, close the ComfyUI console command prompt. 3. "Recommended SD 1.5 Resolution Calculator", open command prompt from this folder \ComfyUI\custom_nodes\ then use this command "git clone https://github.com/marhensa/sd15-recommended-res-calc.git") 4. "ComfyUI ControlNet Aux" custom node can be installed with [this instruction](https://github.com/Fannovel16/comfyui_controlnet_aux#installation), and the models can be downloaded [from here](https://huggingface.co/lllyasviel/sd_control_collection/tree/main), put into \ComfyUI\models\controlnet\ 5. LCM LoRA, (needs a LCM LoRA, download it from here for [LCM-SDXL](https://huggingface.co/latent-consistency/lcm-lora-sdxl/resolve/main/pytorch_lora_weights.safetensors?download=true), for [LCM-SDXL-SSD1B](https://huggingface.co/latent-consistency/lcm-lora-ssd-1b/resolve/main/pytorch_lora_weights.safetensors?download=true), and here for [LCM-SD1.5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5/resolve/main/pytorch_lora_weights.safetensors?download=true)), for LCM-SDXL put into \ComfyUI\models\loras\LCM\SDXL\, LCM-SDXL-SSD1B put into \ComfyUI\models\loras\LCM\SDXL-SSD1B\, LCM-SD1.5 put into \ComfyUI\models\loras\LCM\SD15\ . 6. LCM-Turbo, download from [here](https://civitai.com/api/download/models/246747?type=Model&format=SafeTensor) for SDXL based models, and then put into put into \ComfyUI\models\loras\SDXL\LCMTurboMix_Euler_A_fix.safetensors 7. Another LoRA, download additionals LoRA from CivitAI or select "None" to not use it. 8. VAE selector, (needs a VAE file, download SDXL BF16 VAE from [here](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl_vae.safetensors), and VAE file for SD 1.5 from [here](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors?download=true). Put into \ComfyUI\models\vae\SDXL\ and \ComfyUI\models\vae\SD15\). 9. Upscale model, (needs to be downloaded into \ComfyUI\models\upscale_models\ Recommended one is 4x-UltraSharp, download [from here](https://mega.nz/folder/qZRBmaIY#nIG8KyWFcGNTuMX_XNbJ_g). All the list of Upscale model is [here](https://upscale.wiki/w/index.php?title=Model_Database&oldid=1571)) 10. Checkpoints. SDXL-SSD1B can be downloaded from [here](https://huggingface.co/segmind/SSD-1B/resolve/main/SSD-1B.safetensors?download=true), my recommended Checkpoint for SDXL is [AlbedoBase-XL](https://civitai.com/models/140737), [Jib-Mix-Realistic-XL](https://civitai.com/models/194768), [Crystal-Clear-XL](https://civitai.com/models/122822), and for SD1.5 is [Haveall](https://civitai.com/models/213692), download Safetensors file and put into \ComfyUI\models\checkpoints\SDXL\ and \ComfyUI\models\checkpoints\SD15\ 11. Note: When loading a PNG Workflow from here, first click Refresh on ComfyUI menu, it will refresh models that you have on your PC, then choose it (Checkpoints, VAE, LoRA, Upscale Model, ControlNet models etc) accordingly. . > Custom Node "[Recommended Resolution Calculator](https://github.com/marhensa/sdxl-recommended-res-calc)", makes targeting final resolution much easier, because it will calculates recommended SD initial size and its upscale (or reverse upscale) value. You only need to input your FINAL target resolution with this custom node. . # WORKFLOW SELECTION: (drag and drop that PNG image file into ComfyUI interface, it will open up as workflow template) ## RECOMMENDED! FAST!! TIDY - Single SDXL Checkpoint Workflow (LCM-Turbo, PromptStyler, Upscale Model Switch, ControlNet, FaceDetailer) : ![SDXL-Tidy-LCMTurbo-PromptStyler-UpscaleModelSwitch-ControlNet-FaceDetailer.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/SDXL-Tidy-LCMTurbo-PromptStyler-UpscaleModelSwitch-ControlNet-FaceDetailer.png) (ControlNet image reference example: [halo.jpg](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/halo.jpg)) . ## TIDY - Single SD 1.5 Checkpoint Workflow (LCM, PromptStyler, Upscale Model Switch, ControlNet, FaceDetailer) : ![SD15-Tidy-LCM-PromptStyler-UpscaleModelSwitch-ControlNet-FaceDetailer.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/SD15-Tidy-LCM-PromptStyler-UpscaleModelSwitch-ControlNet-FaceDetailer.png) (ControlNet image reference example: [halo.jpg](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/halo.jpg)) . ## Breakdown - SDXL Workflow (LCM-Turbo, PromptStyler, Upscale Model Switch) : ![sdxl-lcmturbo-workflow.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/sdxl-lcmturbo-workflow.png) . ## Breakdown - SDXL-SSD1B Workflow (LCM, PromptStyler, Upscale Model Switch) : ![sdxl-lcm-workflow.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/sdxlssd1b-lcm-workflow.png) . ## Breakdown - SD1.5 Workflow (LCM, PromptStyler, Upscale Model Switch) : ![sdxl-lcm-workflow.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/sd15-lcm-workflow.png) . ## Simple without install anything - SDXL / SD15 LCM Workflow : ![SDXL_Single_Tidy_Simple-LCM-LoRA-VAE.png](https://huggingface.co/datasets/marhensa/comfyui-workflow/resolve/main/SDXL_Single_Tidy_Simple-LCM-LoRA-VAE.png)

hustzx/sd_test

--- license: apache-2.0 ---

detakarang/know_sql_alpaca

--- license: openrail ---

youngdicey/rico-raw

--- license: openrail ---

gulyaabdullaeva/DS_test

--- license: unknown ---